{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\toddm\Downloads\Diffusion and DMAs\Final Submission Files\Replication Files\Log Final.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res} 5 Apr 2024, 23:26:56

{com}. spmap exposure using "County2.dta" if cont==1, id(_ID)
{res}
{com}. 
. gr_edit .legend.plotregion1.key[1].view.style.editstyle area(shadestyle(color(gs12))) editcopy
{res}
{com}. 
. gr_edit .legend.plotregion1.key[2].view.style.editstyle area(shadestyle(color(black))) editcopy
{res}
{com}. 
. gr_edit .legend.plotregion1.key[3].view.style.editstyle area(shadestyle(color(white))) editcopy
{res}
{com}. 
. gr_edit .plotregion1.plot2.style.editstyle area(linestyle(color(gs12))) editcopy
{res}
{com}. 
. gr_edit .legend.draw_view.setstyle, style(no)
{res}
{com}. 
. gr_edit .style.editstyle boxstyle(linestyle(color(black))) editcopy
{res}
{com}. 
. gr_edit .legend.plotregion1.key[1].xsz.editstyle 5 editcopy
{res}
{com}. 
. gr_edit .legend.plotregion1.key[1].ysz.editstyle 4 editcopy
{res}
{com}. 
. gr_edit .legend.plotregion1.key[2].xsz.editstyle 5 editcopy
{res}
{com}. 
. gr_edit .legend.plotregion1.key[2].ysz.editstyle 4 editcopy
{res}
{com}. 
. gr_edit .legend.plotregion1.key[3].xsz.editstyle 5 editcopy
{res}
{com}. 
. gr_edit .legend.plotregion1.key[3].ysz.editstyle 4 editcopy
{res}
{com}. 
. gr_edit .legend.plotregion1.label[1].text = {c -(}{c )-}
{res}
{com}. 
. gr_edit .legend.plotregion1.label[1].text.Arrpush In-State Media Market Counties
{res}
{com}. 
. gr_edit .legend.plotregion1.label[1].style.editstyle size(2.5) editcopy
{res}
{com}. 
. gr_edit .legend.plotregion1.label[2].text = {c -(}{c )-}
{res}
{com}. 
. gr_edit .legend.plotregion1.label[2].text.Arrpush Out-of-State Media Market Counties
{res}
{com}. 
. gr_edit .legend.plotregion1.label[2].style.editstyle size(2.5) editcopy
{res}
{com}. 
. gr_edit .legend.plotregion1.label[3].text = {c -(}{c )-}
{res}
{com}. 
. gr_edit .legend.plotregion1.label[3].text.Arrpush Not in Study
{res}
{com}. 
. gr_edit .legend.plotregion1.label[3].style.editstyle size(2.5) editcopy
{res}
{com}. use "C:\Users\toddm\Downloads\Diffusion and DMAs\Final Submission Files\Replication Files\ENAPD Replication Dataset.dta"

. do "C:\Users\toddm\AppData\Local\Temp\STD61a8_000000.tmp"
{txt}
{com}. ttest polauth if multauth==1, by(oos_adopt_d)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
      No {c |}{res}{col 12}  9,914{col 22} .0760541{col 34} .0026625{col 46}  .265098{col 58} .0708351{col 70}  .081273
     {txt}Yes {c |}{res}{col 12}    920{col 22} .0913043{col 34} .0095016{col 46} .2881981{col 58}  .072657{col 70} .1099517
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12} 10,834{col 22} .0773491{col 34} .0025667{col 46} .2671569{col 58} .0723179{col 70} .0823803
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22}-.0152503{col 34} .0092068{col 58}-.0332972{col 70} .0027967
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}No{txt}) - mean({res}Yes{txt})                                   t = {res} -1.6564
{txt}Ho: diff = 0                                     degrees of freedom = {res}   10832

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.0488         {txt}Pr(|T| > |t|) = {res}0.0977          {txt}Pr(T > t) = {res}0.9512
{txt}
{com}. ttest polspon , by(oos_adopt_d)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
      No {c |}{res}{col 12} 13,903{col 22} .1252248{col 34} .0028071{col 46} .3309855{col 58} .1197225{col 70}  .130727
     {txt}Yes {c |}{res}{col 12}  1,145{col 22} .1737991{col 34} .0112035{col 46} .3791022{col 58} .1518174{col 70} .1957808
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12} 15,048{col 22} .1289208{col 34} .0027319{col 46} .3351234{col 58} .1235659{col 70} .1342756
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22}-.0485744{col 34} .0102963{col 58}-.0687563{col 70}-.0283924
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}No{txt}) - mean({res}Yes{txt})                                   t = {res} -4.7177
{txt}Ho: diff = 0                                     degrees of freedom = {res}   15046

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}1.0000
{txt}
{com}. *Distinguishing between non-exposure districts and exposure districts where policy wasn’t adopted
. tabstat polauth if multauth==1, by(oos_adopt)

{txt}Summary for variables: polauth
{col 6}by categories of: oos_adopt (has state of out-of-state DMA adopted policy?)

{ralign 16:oos_adopt} {...}
{c |}      mean
{hline 17}{c +}{hline 10}
{ralign 16:has not adopted} {...}
{c |}{...}
 {res} .0458716
{txt}{ralign 16:has adopted} {...}
{c |}{...}
 {res} .0913043
{txt}{ralign 16:not an exposure } {...}
{c |}{...}
 {res} .0802065
{txt}{hline 17}{c +}{hline 10}
{ralign 16:Total} {...}
{c |}{...}
 {res} .0773491
{txt}{hline 17}{c BT}{hline 10}

{com}. tabstat polspon , by(oos_adopt)

{txt}Summary for variables: polspon
{col 6}by categories of: oos_adopt (has state of out-of-state DMA adopted policy?)

{ralign 16:oos_adopt} {...}
{c |}      mean
{hline 17}{c +}{hline 10}
{ralign 16:has not adopted} {...}
{c |}{...}
 {res} .1312349
{txt}{ralign 16:has adopted} {...}
{c |}{...}
 {res} .1737991
{txt}{ralign 16:not an exposure } {...}
{c |}{...}
 {res} .1241764
{txt}{hline 17}{c +}{hline 10}
{ralign 16:Total} {...}
{c |}{...}
 {res} .1289208
{txt}{hline 17}{c BT}{hline 10}

{com}. ttest polauth if multauth==1 & oos_adopt!=99, by(oos_adopt)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
 has not {c |}{res}{col 12}  1,199{col 22} .0458716{col 34} .0060443{col 46} .2092938{col 58} .0340129{col 70} .0577302
{txt}has adop {c |}{res}{col 12}    920{col 22} .0913043{col 34} .0095016{col 46} .2881981{col 58}  .072657{col 70} .1099517
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}  2,119{col 22}  .065597{col 34} .0053796{col 46} .2476347{col 58} .0550472{col 70} .0761467
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22}-.0454328{col 34} .0108112{col 58}-.0666344{col 70}-.0242312
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}has not{txt}) - mean({res}has adop{txt})                         t = {res} -4.2024
{txt}Ho: diff = 0                                     degrees of freedom = {res}    2117

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}1.0000
{txt}
{com}. ttest polspon if oos_adopt!=99, by(oos_adopt)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
 has not {c |}{res}{col 12}  2,065{col 22} .1312349{col 34} .0074323{col 46} .3377388{col 58} .1166594{col 70} .1458104
{txt}has adop {c |}{res}{col 12}  1,145{col 22} .1737991{col 34} .0112035{col 46} .3791022{col 58} .1518174{col 70} .1957808
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}  3,210{col 22} .1464174{col 34} .0062407{col 46} .3535793{col 58} .1341813{col 70} .1586536
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22}-.0425643{col 34} .0130083{col 58}-.0680697{col 70}-.0170588
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}has not{txt}) - mean({res}has adop{txt})                         t = {res} -3.2721
{txt}Ho: diff = 0                                     degrees of freedom = {res}    3208

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.0005         {txt}Pr(|T| > |t|) = {res}0.0011          {txt}Pr(T > t) = {res}0.9995
{txt}
{com}. 
. *Main models (main text and Table A-4)
. *Authorship models
. quietly melogit polauth oos_adopt_d judic chair leader female majmemb blackpct srty senate squire introlim termlim c.polcons##c.smideol if multauth==1 
{txt}
{com}. est store a1
{txt}
{com}. quietly melogit polauth oos_adopt_d judic chair leader female majmemb blackpct srty senate squire introlim termlim c.polcons##c.smideol if multauth==1 || _all: R.polnum
{txt}
{com}. matrix a1 = e(b)
{txt}
{com}. est store a2
{txt}
{com}. melogit polauth oos_adopt_d judic chair leader female majmemb blackpct srty senate squire introlim termlim c.polcons##c.smideol  if multauth==1 || _all: R.polnum|| statenum:, from(a1) difficult
{res}{txt}{p 0 0 2}
note: crossed random-effects model specified; option intmethod(laplace) implied
{p_end}

Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-2820.2159}  
Iteration 1:{space 3}log likelihood = {res:-2562.2582}  
Iteration 2:{space 3}log likelihood = {res:-2551.9787}  
Iteration 3:{space 3}log likelihood = {res:-2551.8974}  
Iteration 4:{space 3}log likelihood = {res:-2551.8974}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-2399.2817}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-2399.2817}  (not concave)
Iteration 1:{space 3}log likelihood = {res:-2394.7889}  (not concave)
Iteration 2:{space 3}log likelihood = {res:-2394.0789}  (not concave)
Iteration 3:{space 3}log likelihood = {res:-2391.1222}  (not concave)
Iteration 4:{space 3}log likelihood = {res:-2389.6465}  (not concave)
Iteration 5:{space 3}log likelihood = {res:-2388.1329}  (not concave)
Iteration 6:{space 3}log likelihood = {res:-2386.5456}  
Iteration 7:{space 3}log likelihood = {res:-2384.7423}  
Iteration 8:{space 3}log likelihood = {res:-2384.5163}  
Iteration 9:{space 3}log likelihood = {res:-2384.4762}  
Iteration 10:{space 2}log likelihood = {res:-2384.4497}  
Iteration 11:{space 2}log likelihood = {res: -2384.438}  
Iteration 12:{space 2}log likelihood = {res:-2384.4243}  
Iteration 13:{space 2}log likelihood = {res:-2384.4212}  
Iteration 14:{space 2}log likelihood = {res:-2384.4193}  
Iteration 15:{space 2}log likelihood = {res:-2384.4193}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    10,834

{txt}{hline 16}{c TT}{hline 44}
{col 17}{txt}{c |}{col 23}No. of{col 36}Observations per Group
{col 2}{txt}Group Variable{col 17}{c |}{col 23}Groups{col 33}Minimum{col 44}Average{col 55}Maximum
{txt}{hline 16}{c +}{hline 44}
{col 12}{res}_all{col 17}{txt}{c |}{res}{col 21}       1{col 31}   10,834{col 42} 10,834.0{col 53}   10,834
{col 8}{res}statenum{col 17}{txt}{c |}{res}{col 21}      14{col 31}      343{col 42}    773.9{col 53}    1,273
{txt}{hline 16}{c BT}{hline 44}

{txt}Integration method: {col 25}{res}laplace

{col 49}{txt}Wald chi2({res}15{txt}){col 67}={res}{col 70}   303.85
{txt}Log likelihood = {res}-2384.4193{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}            polauth{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}oos_adopt_d {c |}{col 21}{res}{space 2} .3277807{col 33}{space 2} .1384559{col 44}{space 1}    2.37{col 53}{space 3}0.018{col 61}{space 4} .0564122{col 74}{space 3} .5991493
{txt}{space 14}judic {c |}{col 21}{res}{space 2} .5782734{col 33}{space 2} .0959021{col 44}{space 1}    6.03{col 53}{space 3}0.000{col 61}{space 4} .3903087{col 74}{space 3}  .766238
{txt}{space 14}chair {c |}{col 21}{res}{space 2} .7568974{col 33}{space 2} .2592158{col 44}{space 1}    2.92{col 53}{space 3}0.004{col 61}{space 4} .2488438{col 74}{space 3} 1.264951
{txt}{space 13}leader {c |}{col 21}{res}{space 2} .1304601{col 33}{space 2}  .150782{col 44}{space 1}    0.87{col 53}{space 3}0.387{col 61}{space 4}-.1650673{col 74}{space 3} .4259875
{txt}{space 13}female {c |}{col 21}{res}{space 2} .1243375{col 33}{space 2} .0962897{col 44}{space 1}    1.29{col 53}{space 3}0.197{col 61}{space 4}-.0643869{col 74}{space 3}  .313062
{txt}{space 12}majmemb {c |}{col 21}{res}{space 2} .2568618{col 33}{space 2} .0888576{col 44}{space 1}    2.89{col 53}{space 3}0.004{col 61}{space 4} .0827042{col 74}{space 3} .4310195
{txt}{space 11}blackpct {c |}{col 21}{res}{space 2}-.1649159{col 33}{space 2} .2779787{col 44}{space 1}   -0.59{col 53}{space 3}0.553{col 61}{space 4}-.7097442{col 74}{space 3} .3799123
{txt}{space 15}srty {c |}{col 21}{res}{space 2} -.015275{col 33}{space 2} .0066911{col 44}{space 1}   -2.28{col 53}{space 3}0.022{col 61}{space 4}-.0283894{col 74}{space 3}-.0021606
{txt}{space 13}senate {c |}{col 21}{res}{space 2} .5882751{col 33}{space 2} .1020965{col 44}{space 1}    5.76{col 53}{space 3}0.000{col 61}{space 4} .3881695{col 74}{space 3} .7883806
{txt}{space 13}squire {c |}{col 21}{res}{space 2}  4.16321{col 33}{space 2} 1.913239{col 44}{space 1}    2.18{col 53}{space 3}0.030{col 61}{space 4} .4133298{col 74}{space 3}  7.91309
{txt}{space 11}introlim {c |}{col 21}{res}{space 2}-.9694656{col 33}{space 2} .5818608{col 44}{space 1}   -1.67{col 53}{space 3}0.096{col 61}{space 4}-2.109892{col 74}{space 3} .1709605
{txt}{space 12}termlim {c |}{col 21}{res}{space 2} .3406696{col 33}{space 2} .2345738{col 44}{space 1}    1.45{col 53}{space 3}0.146{col 61}{space 4}-.1190866{col 74}{space 3} .8004259
{txt}{space 12}polcons {c |}{col 21}{res}{space 2} 2.003568{col 33}{space 2} .9394981{col 44}{space 1}    2.13{col 53}{space 3}0.033{col 61}{space 4} .1621857{col 74}{space 3}  3.84495
{txt}{space 12}smideol {c |}{col 21}{res}{space 2} -2.28425{col 33}{space 2} .2059364{col 44}{space 1}  -11.09{col 53}{space 3}0.000{col 61}{space 4}-2.687878{col 74}{space 3}-1.880622
{txt}{space 19} {c |}
c.polcons#c.smideol {c |}{col 21}{res}{space 2} 5.033859{col 33}{space 2} .4011433{col 44}{space 1}   12.55{col 53}{space 3}0.000{col 61}{space 4} 4.247633{col 74}{space 3} 5.820086
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2}-5.150451{col 33}{space 2} .6277995{col 44}{space 1}   -8.20{col 53}{space 3}0.000{col 61}{space 4}-6.380916{col 74}{space 3}-3.919987
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}_all>polnum        {col 21}{txt}{c |}
{space 10}var(_cons){c |}{col 21}{res}{space 2} .2341277{col 33}{space 2} .1113332{col 61}{space 4} .0921906{col 74}{space 3}  .594592
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}statenum           {col 21}{txt}{c |}
{space 10}var(_cons){c |}{col 21}{res}{space 2} .5021741{col 33}{space 2} .2046254{col 61}{space 4} .2259476{col 74}{space 3} 1.116095
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}chi2({res}2{txt}) ={res} 334.96{col 59}{txt}Prob > chi2 ={res}{col 73}0.0000

{txt}{p 0 6 4 79}Note: {help j_mixedlr##|_new:LR test is conservative} and provided only for reference.{p_end}

{com}. est store a3
{txt}
{com}. margins, at(oos_adopt_d=(0 1)) 
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}    10,834
{txt}{col 1}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:oos_adopt_d}{space 5}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:oos_adopt_d}{space 5}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .0689358{col 26}{space 2} .0142229{col 37}{space 1}    4.85{col 46}{space 3}0.000{col 54}{space 4} .0410594{col 67}{space 3} .0968123
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .0892714{col 26}{space 2} .0193899{col 37}{space 1}    4.60{col 46}{space 3}0.000{col 54}{space 4}  .051268{col 67}{space 3} .1272749
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, dydx(oos_adopt_d) post
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}    10,834
{txt}{col 1}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:oos_adopt_d}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}oos_adopt_d {c |}{col 14}{res}{space 2} .0185389{col 26}{space 2} .0083859{col 37}{space 1}    2.21{col 46}{space 3}0.027{col 54}{space 4} .0021029{col 67}{space 3}  .034975
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. est store all_auth
{txt}
{com}. lrtest a1 a2

{txt}Likelihood-ratio test{col 55}LR chi2({res}1{txt}){col 67}={res}     58.30
{txt}(Assumption: {res}{stata est replay a1:a1}{txt} nested in {res}{stata est replay a2:a2}{txt}){col 55}Prob > chi2 = {res}   0.0000
{txt}
{com}. lrtest a2 a3

{txt}Likelihood-ratio test{col 55}LR chi2({res}1{txt}){col 67}={res}    276.66
{txt}(Assumption: {res}{stata est replay a2:a2}{txt} nested in {res}{stata est replay a3:a3}{txt}){col 55}Prob > chi2 = {res}   0.0000
{txt}
{com}. *Sponsorship models
. quietly melogit polspon oos_adopt_d judic chair leader female majmemb blackpct srty senate squire sponlim termlim c.polcons##c.smideol
{txt}
{com}. est store b1
{txt}
{com}. quietly melogit polspon oos_adopt_d judic chair leader female majmemb blackpct srty senate squire sponlim termlim c.polcons##c.smideol || _all: R.polnum
{txt}
{com}. matrix b1 = e(b)
{txt}
{com}. est store b2
{txt}
{com}. melogit polspon oos_adopt_d judic chair leader female majmemb blackpct srty senate squire sponlim termlim c.polcons##c.smideol || _all: R.polnum|| statenum:, from(b1) difficult
{res}{txt}{p 0 0 2}
note: crossed random-effects model specified; option intmethod(laplace) implied
{p_end}

Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-5372.8063}  
Iteration 1:{space 3}log likelihood = {res:-5191.5564}  
Iteration 2:{space 3}log likelihood = {res:-5188.7795}  
Iteration 3:{space 3}log likelihood = {res:-5188.7776}  
Iteration 4:{space 3}log likelihood = {res:-5188.7776}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-4947.8863}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-4947.8863}  (not concave)
Iteration 1:{space 3}log likelihood = {res:-4912.3657}  (not concave)
Iteration 2:{space 3}log likelihood = {res:-4911.9638}  
Iteration 3:{space 3}log likelihood = {res:-4911.8322}  
Iteration 4:{space 3}log likelihood = {res:-4911.8256}  
Iteration 5:{space 3}log likelihood = {res:-4911.8255}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    15,048

{txt}{hline 16}{c TT}{hline 44}
{col 17}{txt}{c |}{col 23}No. of{col 36}Observations per Group
{col 2}{txt}Group Variable{col 17}{c |}{col 23}Groups{col 33}Minimum{col 44}Average{col 55}Maximum
{txt}{hline 16}{c +}{hline 44}
{col 12}{res}_all{col 17}{txt}{c |}{res}{col 21}       1{col 31}   15,048{col 42} 15,048.0{col 53}   15,048
{col 8}{res}statenum{col 17}{txt}{c |}{res}{col 21}      20{col 31}      341{col 42}    752.4{col 53}    1,273
{txt}{hline 16}{c BT}{hline 44}

{txt}Integration method: {col 25}{res}laplace

{col 49}{txt}Wald chi2({res}15{txt}){col 67}={res}{col 70}   631.08
{txt}Log likelihood = {res}-4911.8255{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}            polspon{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}oos_adopt_d {c |}{col 21}{res}{space 2}  .328353{col 33}{space 2} .0928525{col 44}{space 1}    3.54{col 53}{space 3}0.000{col 61}{space 4} .1463654{col 74}{space 3} .5103405
{txt}{space 14}judic {c |}{col 21}{res}{space 2} .4442067{col 33}{space 2} .0645323{col 44}{space 1}    6.88{col 53}{space 3}0.000{col 61}{space 4} .3177257{col 74}{space 3} .5706876
{txt}{space 14}chair {c |}{col 21}{res}{space 2} .5486235{col 33}{space 2} .1947775{col 44}{space 1}    2.82{col 53}{space 3}0.005{col 61}{space 4} .1668667{col 74}{space 3} .9303803
{txt}{space 13}leader {c |}{col 21}{res}{space 2} .1634027{col 33}{space 2} .1022996{col 44}{space 1}    1.60{col 53}{space 3}0.110{col 61}{space 4}-.0371008{col 74}{space 3} .3639062
{txt}{space 13}female {c |}{col 21}{res}{space 2} .0282358{col 33}{space 2} .0666603{col 44}{space 1}    0.42{col 53}{space 3}0.672{col 61}{space 4} -.102416{col 74}{space 3} .1588876
{txt}{space 12}majmemb {c |}{col 21}{res}{space 2} .2065401{col 33}{space 2} .0562308{col 44}{space 1}    3.67{col 53}{space 3}0.000{col 61}{space 4} .0963297{col 74}{space 3} .3167505
{txt}{space 11}blackpct {c |}{col 21}{res}{space 2}-.3010264{col 33}{space 2} .1837915{col 44}{space 1}   -1.64{col 53}{space 3}0.101{col 61}{space 4}-.6612511{col 74}{space 3} .0591982
{txt}{space 15}srty {c |}{col 21}{res}{space 2}-.0304773{col 33}{space 2} .0045607{col 44}{space 1}   -6.68{col 53}{space 3}0.000{col 61}{space 4} -.039416{col 74}{space 3}-.0215385
{txt}{space 13}senate {c |}{col 21}{res}{space 2}  .753613{col 33}{space 2} .0700185{col 44}{space 1}   10.76{col 53}{space 3}0.000{col 61}{space 4} .6163792{col 74}{space 3} .8908468
{txt}{space 13}squire {c |}{col 21}{res}{space 2} 4.884418{col 33}{space 2} 1.583391{col 44}{space 1}    3.08{col 53}{space 3}0.002{col 61}{space 4} 1.781029{col 74}{space 3} 7.987808
{txt}{space 12}sponlim {c |}{col 21}{res}{space 2} .0813056{col 33}{space 2}  .217243{col 44}{space 1}    0.37{col 53}{space 3}0.708{col 61}{space 4}-.3444828{col 74}{space 3} .5070941
{txt}{space 12}termlim {c |}{col 21}{res}{space 2} .6892739{col 33}{space 2} .2171031{col 44}{space 1}    3.17{col 53}{space 3}0.001{col 61}{space 4} .2637596{col 74}{space 3} 1.114788
{txt}{space 12}polcons {c |}{col 21}{res}{space 2} 2.459112{col 33}{space 2} .8450886{col 44}{space 1}    2.91{col 53}{space 3}0.004{col 61}{space 4} .8027684{col 74}{space 3} 4.115455
{txt}{space 12}smideol {c |}{col 21}{res}{space 2}-2.189391{col 33}{space 2} .1346666{col 44}{space 1}  -16.26{col 53}{space 3}0.000{col 61}{space 4}-2.453333{col 74}{space 3} -1.92545
{txt}{space 19} {c |}
c.polcons#c.smideol {c |}{col 21}{res}{space 2} 4.749645{col 33}{space 2} .2612494{col 44}{space 1}   18.18{col 53}{space 3}0.000{col 61}{space 4} 4.237606{col 74}{space 3} 5.261685
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2}-4.787989{col 33}{space 2} .5341052{col 44}{space 1}   -8.96{col 53}{space 3}0.000{col 61}{space 4}-5.834816{col 74}{space 3}-3.741162
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}_all>polnum        {col 21}{txt}{c |}
{space 10}var(_cons){c |}{col 21}{res}{space 2} .2257078{col 33}{space 2} .1018184{col 61}{space 4} .0932316{col 74}{space 3}  .546424
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}statenum           {col 21}{txt}{c |}
{space 10}var(_cons){c |}{col 21}{res}{space 2} .6191964{col 33}{space 2} .2170172{col 61}{space 4} .3115283{col 74}{space 3}  1.23072
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}chi2({res}2{txt}) ={res} 553.90{col 59}{txt}Prob > chi2 ={res}{col 73}0.0000

{txt}{p 0 6 4 79}Note: {help j_mixedlr##|_new:LR test is conservative} and provided only for reference.{p_end}

{com}. est store b3
{txt}
{com}. margins, at(oos_adopt_d=(0 1))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}    15,048
{txt}{col 1}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:oos_adopt_d}{space 5}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:oos_adopt_d}{space 5}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} .1275348{col 26}{space 2} .0215479{col 37}{space 1}    5.92{col 46}{space 3}0.000{col 54}{space 4} .0853017{col 67}{space 3} .1697678
{txt}{space 10}2  {c |}{col 14}{res}{space 2} .1602571{col 26}{space 2} .0267279{col 37}{space 1}    6.00{col 46}{space 3}0.000{col 54}{space 4} .1078714{col 67}{space 3} .2126427
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, dydx(oos_adopt_d) post
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}    15,048
{txt}{col 1}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:oos_adopt_d}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}oos_adopt_d {c |}{col 14}{res}{space 2} .0303886{col 26}{space 2} .0093316{col 37}{space 1}    3.26{col 46}{space 3}0.001{col 54}{space 4} .0120989{col 67}{space 3} .0486783
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. est store all_spon
{txt}
{com}. lrtest b1 b2

{txt}Likelihood-ratio test{col 55}LR chi2({res}1{txt}){col 67}={res}    180.69
{txt}(Assumption: {res}{stata est replay b1:b1}{txt} nested in {res}{stata est replay b2:b2}{txt}){col 55}Prob > chi2 = {res}   0.0000
{txt}
{com}. lrtest b2 b3

{txt}Likelihood-ratio test{col 55}LR chi2({res}1{txt}){col 67}={res}    373.22
{txt}(Assumption: {res}{stata est replay b2:b2}{txt} nested in {res}{stata est replay b3:b3}{txt}){col 55}Prob > chi2 = {res}   0.0000
{txt}
{com}. 
. *Core criminal justice policies vs. others (Main text and Table A-6)
. *Authorship models
. *Core policies
. quietly melogit polauth oos_adopt_d judic chair leader female majmemb blackpct srty senate squire introlim termlim c.polcons##c.smideol if multauth==1 & purecj==1 || _all: R.polnum
{txt}
{com}. matrix c1 = e(b)
{txt}
{com}. melogit polauth oos_adopt_d judic chair leader female majmemb blackpct srty senate squire introlim termlim c.polcons##c.smideol  if multauth==1 & purecj==1 || _all: R.polnum|| statenum:, from(c1) difficult
{res}{txt}{p 0 0 2}
note: crossed random-effects model specified; option intmethod(laplace) implied
{p_end}

Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-1355.5287}  
Iteration 1:{space 3}log likelihood = {res:-1252.4591}  
Iteration 2:{space 3}log likelihood = {res:-1248.0728}  
Iteration 3:{space 3}log likelihood = {res: -1248.015}  
Iteration 4:{space 3}log likelihood = {res: -1248.015}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-1187.0681}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-1187.0681}  
Iteration 1:{space 3}log likelihood = {res:-1183.4794}  (not concave)
Iteration 2:{space 3}log likelihood = {res:-1180.4235}  (not concave)
Iteration 3:{space 3}log likelihood = {res:-1180.3568}  (not concave)
Iteration 4:{space 3}log likelihood = {res: -1180.032}  
Iteration 5:{space 3}log likelihood = {res:-1166.8896}  (not concave)
Iteration 6:{space 3}log likelihood = {res:-1166.1863}  (not concave)
Iteration 7:{space 3}log likelihood = {res:-1166.1337}  
Iteration 8:{space 3}log likelihood = {res:-1166.1129}  
Iteration 9:{space 3}log likelihood = {res:-1166.0992}  
Iteration 10:{space 2}log likelihood = {res:-1166.0902}  
Iteration 11:{space 2}log likelihood = {res:-1166.0822}  
Iteration 12:{space 2}log likelihood = {res:-1166.0774}  
Iteration 13:{space 2}log likelihood = {res:-1166.0741}  
Iteration 14:{space 2}log likelihood = {res:-1166.0718}  
Iteration 15:{space 2}log likelihood = {res:-1166.0701}  
Iteration 16:{space 2}log likelihood = {res:-1166.0689}  
Iteration 17:{space 2}log likelihood = {res:-1166.0679}  
Iteration 18:{space 2}log likelihood = {res:-1166.0673}  
Iteration 19:{space 2}log likelihood = {res:-1166.0656}  
Iteration 20:{space 2}log likelihood = {res:-1166.0656}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     4,727

{txt}{hline 16}{c TT}{hline 44}
{col 17}{txt}{c |}{col 23}No. of{col 36}Observations per Group
{col 2}{txt}Group Variable{col 17}{c |}{col 23}Groups{col 33}Minimum{col 44}Average{col 55}Maximum
{txt}{hline 16}{c +}{hline 44}
{col 12}{res}_all{col 17}{txt}{c |}{res}{col 21}       1{col 31}    4,727{col 42}  4,727.0{col 53}    4,727
{col 8}{res}statenum{col 17}{txt}{c |}{res}{col 21}      14{col 31}       98{col 42}    337.6{col 53}      634
{txt}{hline 16}{c BT}{hline 44}

{txt}Integration method: {col 25}{res}laplace

{col 49}{txt}Wald chi2({res}15{txt}){col 67}={res}{col 70}   140.83
{txt}Log likelihood = {res}-1166.0656{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}            polauth{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}oos_adopt_d {c |}{col 21}{res}{space 2} .4923254{col 33}{space 2} .1782905{col 44}{space 1}    2.76{col 53}{space 3}0.006{col 61}{space 4} .1428823{col 74}{space 3} .8417684
{txt}{space 14}judic {c |}{col 21}{res}{space 2} .3771334{col 33}{space 2} .1400196{col 44}{space 1}    2.69{col 53}{space 3}0.007{col 61}{space 4} .1026999{col 74}{space 3} .6515668
{txt}{space 14}chair {c |}{col 21}{res}{space 2} .6253212{col 33}{space 2} .3991195{col 44}{space 1}    1.57{col 53}{space 3}0.117{col 61}{space 4}-.1569387{col 74}{space 3} 1.407581
{txt}{space 13}leader {c |}{col 21}{res}{space 2}-.0492151{col 33}{space 2} .2391772{col 44}{space 1}   -0.21{col 53}{space 3}0.837{col 61}{space 4}-.5179939{col 74}{space 3} .4195637
{txt}{space 13}female {c |}{col 21}{res}{space 2} .2303396{col 33}{space 2} .1360334{col 44}{space 1}    1.69{col 53}{space 3}0.090{col 61}{space 4} -.036281{col 74}{space 3} .4969601
{txt}{space 12}majmemb {c |}{col 21}{res}{space 2} .2205945{col 33}{space 2} .1312734{col 44}{space 1}    1.68{col 53}{space 3}0.093{col 61}{space 4}-.0366967{col 74}{space 3} .4778856
{txt}{space 11}blackpct {c |}{col 21}{res}{space 2}-1.022743{col 33}{space 2} .4223285{col 44}{space 1}   -2.42{col 53}{space 3}0.015{col 61}{space 4}-1.850491{col 74}{space 3}-.1949938
{txt}{space 15}srty {c |}{col 21}{res}{space 2}-.0181101{col 33}{space 2} .0094744{col 44}{space 1}   -1.91{col 53}{space 3}0.056{col 61}{space 4}-.0366795{col 74}{space 3} .0004593
{txt}{space 13}senate {c |}{col 21}{res}{space 2}  .340565{col 33}{space 2} .1607484{col 44}{space 1}    2.12{col 53}{space 3}0.034{col 61}{space 4} .0255039{col 74}{space 3} .6556261
{txt}{space 13}squire {c |}{col 21}{res}{space 2}  3.58264{col 33}{space 2} 2.622259{col 44}{space 1}    1.37{col 53}{space 3}0.172{col 61}{space 4}-1.556894{col 74}{space 3} 8.722174
{txt}{space 11}introlim {c |}{col 21}{res}{space 2}-.9106276{col 33}{space 2}   .77937{col 44}{space 1}   -1.17{col 53}{space 3}0.243{col 61}{space 4}-2.438165{col 74}{space 3} .6169096
{txt}{space 12}termlim {c |}{col 21}{res}{space 2} 1.880185{col 33}{space 2} .4149475{col 44}{space 1}    4.53{col 53}{space 3}0.000{col 61}{space 4} 1.066903{col 74}{space 3} 2.693467
{txt}{space 12}polcons {c |}{col 21}{res}{space 2} .3108152{col 33}{space 2}  2.45906{col 44}{space 1}    0.13{col 53}{space 3}0.899{col 61}{space 4}-4.508855{col 74}{space 3} 5.130485
{txt}{space 12}smideol {c |}{col 21}{res}{space 2}-2.319521{col 33}{space 2} .3946463{col 44}{space 1}   -5.88{col 53}{space 3}0.000{col 61}{space 4}-3.093014{col 74}{space 3}-1.546029
{txt}{space 19} {c |}
c.polcons#c.smideol {c |}{col 21}{res}{space 2} 4.783406{col 33}{space 2} .7022567{col 44}{space 1}    6.81{col 53}{space 3}0.000{col 61}{space 4} 3.407008{col 74}{space 3} 6.159804
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2}-4.258317{col 33}{space 2}  1.34813{col 44}{space 1}   -3.16{col 53}{space 3}0.002{col 61}{space 4}-6.900604{col 74}{space 3}-1.616031
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}_all>polnum        {col 21}{txt}{c |}
{space 10}var(_cons){c |}{col 21}{res}{space 2} .4192345{col 33}{space 2} .3815944{col 61}{space 4} .0704172{col 74}{space 3} 2.495946
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}statenum           {col 21}{txt}{c |}
{space 10}var(_cons){c |}{col 21}{res}{space 2} .8222432{col 33}{space 2} .3749431{col 61}{space 4} .3363971{col 74}{space 3} 2.009779
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}chi2({res}2{txt}) ={res} 163.90{col 59}{txt}Prob > chi2 ={res}{col 73}0.0000

{txt}{p 0 6 4 79}Note: {help j_mixedlr##|_new:LR test is conservative} and provided only for reference.{p_end}

{com}. *Other policies
. quietly melogit polauth oos_adopt_d judic chair leader female majmemb blackpct srty senate squire introlim termlim c.polcons##c.smideol if multauth==1 & purecj==0 || _all: R.polnum
{txt}
{com}. matrix c2 = e(b)
{txt}
{com}. melogit polauth oos_adopt_d judic chair leader female majmemb blackpct srty senate squire introlim termlim c.polcons##c.smideol  if multauth==1 & purecj==0 || _all: R.polnum|| statenum:, from(c2) difficult
{res}{txt}{p 0 0 2}
note: crossed random-effects model specified; option intmethod(laplace) implied
{p_end}

Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-1441.5178}  
Iteration 1:{space 3}log likelihood = {res:-1286.1363}  
Iteration 2:{space 3}log likelihood = {res:-1282.5999}  
Iteration 3:{space 3}log likelihood = {res:-1282.5765}  
Iteration 4:{space 3}log likelihood = {res:-1282.5765}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res: -1200.729}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res: -1200.729}  
Iteration 1:{space 3}log likelihood = {res:-1198.8676}  (not concave)
Iteration 2:{space 3}log likelihood = {res: -1197.655}  
Iteration 3:{space 3}log likelihood = {res:-1195.9408}  (not concave)
Iteration 4:{space 3}log likelihood = {res:-1195.9079}  (not concave)
Iteration 5:{space 3}log likelihood = {res:-1195.8954}  (not concave)
Iteration 6:{space 3}log likelihood = {res:-1195.8942}  
Iteration 7:{space 3}log likelihood = {res:-1195.8908}  
Iteration 8:{space 3}log likelihood = {res: -1195.888}  
Iteration 9:{space 3}log likelihood = {res:-1195.8868}  
Iteration 10:{space 2}log likelihood = {res:-1195.8868}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     6,107

{txt}{hline 16}{c TT}{hline 44}
{col 17}{txt}{c |}{col 23}No. of{col 36}Observations per Group
{col 2}{txt}Group Variable{col 17}{c |}{col 23}Groups{col 33}Minimum{col 44}Average{col 55}Maximum
{txt}{hline 16}{c +}{hline 44}
{col 12}{res}_all{col 17}{txt}{c |}{res}{col 21}       1{col 31}    6,107{col 42}  6,107.0{col 53}    6,107
{col 8}{res}statenum{col 17}{txt}{c |}{res}{col 21}      14{col 31}      147{col 42}    436.2{col 53}      680
{txt}{hline 16}{c BT}{hline 44}

{txt}Integration method: {col 25}{res}laplace

{col 49}{txt}Wald chi2({res}15{txt}){col 67}={res}{col 70}   193.45
{txt}Log likelihood = {res}-1195.8868{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}            polauth{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}oos_adopt_d {c |}{col 21}{res}{space 2} .0126943{col 33}{space 2} .2309954{col 44}{space 1}    0.05{col 53}{space 3}0.956{col 61}{space 4}-.4400484{col 74}{space 3} .4654369
{txt}{space 14}judic {c |}{col 21}{res}{space 2} .7664817{col 33}{space 2} .1346211{col 44}{space 1}    5.69{col 53}{space 3}0.000{col 61}{space 4} .5026291{col 74}{space 3} 1.030334
{txt}{space 14}chair {c |}{col 21}{res}{space 2} .8977079{col 33}{space 2} .3464393{col 44}{space 1}    2.59{col 53}{space 3}0.010{col 61}{space 4} .2186993{col 74}{space 3} 1.576716
{txt}{space 13}leader {c |}{col 21}{res}{space 2} .2677971{col 33}{space 2} .1968411{col 44}{space 1}    1.36{col 53}{space 3}0.174{col 61}{space 4}-.1180044{col 74}{space 3} .6535985
{txt}{space 13}female {c |}{col 21}{res}{space 2}  .048916{col 33}{space 2} .1391361{col 44}{space 1}    0.35{col 53}{space 3}0.725{col 61}{space 4}-.2237857{col 74}{space 3} .3216177
{txt}{space 12}majmemb {c |}{col 21}{res}{space 2} .2482607{col 33}{space 2}  .124687{col 44}{space 1}    1.99{col 53}{space 3}0.046{col 61}{space 4} .0038786{col 74}{space 3} .4926429
{txt}{space 11}blackpct {c |}{col 21}{res}{space 2}  .595102{col 33}{space 2} .3744387{col 44}{space 1}    1.59{col 53}{space 3}0.112{col 61}{space 4}-.1387844{col 74}{space 3} 1.328988
{txt}{space 15}srty {c |}{col 21}{res}{space 2}-.0171529{col 33}{space 2} .0098467{col 44}{space 1}   -1.74{col 53}{space 3}0.082{col 61}{space 4} -.036452{col 74}{space 3} .0021462
{txt}{space 13}senate {c |}{col 21}{res}{space 2} .7835202{col 33}{space 2} .1406339{col 44}{space 1}    5.57{col 53}{space 3}0.000{col 61}{space 4} .5078828{col 74}{space 3} 1.059158
{txt}{space 13}squire {c |}{col 21}{res}{space 2} 3.302598{col 33}{space 2} 2.328137{col 44}{space 1}    1.42{col 53}{space 3}0.156{col 61}{space 4}-1.260466{col 74}{space 3} 7.865661
{txt}{space 11}introlim {c |}{col 21}{res}{space 2}-1.039697{col 33}{space 2} .6496542{col 44}{space 1}   -1.60{col 53}{space 3}0.110{col 61}{space 4}-2.312996{col 74}{space 3} .2336023
{txt}{space 12}termlim {c |}{col 21}{res}{space 2}-.2178857{col 33}{space 2} .3088429{col 44}{space 1}   -0.71{col 53}{space 3}0.481{col 61}{space 4}-.8232067{col 74}{space 3} .3874353
{txt}{space 12}polcons {c |}{col 21}{res}{space 2} 2.289915{col 33}{space 2} 1.038689{col 44}{space 1}    2.20{col 53}{space 3}0.027{col 61}{space 4} .2541219{col 74}{space 3} 4.325708
{txt}{space 12}smideol {c |}{col 21}{res}{space 2}-2.274338{col 33}{space 2} .2635589{col 44}{space 1}   -8.63{col 53}{space 3}0.000{col 61}{space 4}-2.790904{col 74}{space 3}-1.757772
{txt}{space 19} {c |}
c.polcons#c.smideol {c |}{col 21}{res}{space 2} 5.373788{col 33}{space 2} .5611846{col 44}{space 1}    9.58{col 53}{space 3}0.000{col 61}{space 4} 4.273886{col 74}{space 3} 6.473689
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2}-5.158557{col 33}{space 2} .7228148{col 44}{space 1}   -7.14{col 53}{space 3}0.000{col 61}{space 4}-6.575248{col 74}{space 3}-3.741866
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}_all>polnum        {col 21}{txt}{c |}
{space 10}var(_cons){c |}{col 21}{res}{space 2} .1134818{col 33}{space 2} .0775709{col 61}{space 4} .0297224{col 74}{space 3} .4332797
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}statenum           {col 21}{txt}{c |}
{space 10}var(_cons){c |}{col 21}{res}{space 2} .6017179{col 33}{space 2} .2582177{col 61}{space 4} .2594857{col 74}{space 3} 1.395316
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}chi2({res}2{txt}) ={res} 173.38{col 59}{txt}Prob > chi2 ={res}{col 73}0.0000

{txt}{p 0 6 4 79}Note: {help j_mixedlr##|_new:LR test is conservative} and provided only for reference.{p_end}

{com}. *Sponsorship models
. *Core policies
. quietly melogit polspon oos_adopt_d judic chair leader female majmemb blackpct srty senate squire sponlim termlim c.polcons##c.smideol if purecj==1 || _all: R.polnum
{txt}
{com}. matrix c3 = e(b)
{txt}
{com}. melogit polspon oos_adopt_d judic chair leader female majmemb blackpct srty senate squire sponlim termlim c.polcons##c.smideol if purecj==1 || _all: R.polnum|| statenum:, from(c3) difficult
{res}{txt}{p 0 0 2}
note: crossed random-effects model specified; option intmethod(laplace) implied
{p_end}

Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-2405.0009}  
Iteration 1:{space 3}log likelihood = {res:-2328.4782}  
Iteration 2:{space 3}log likelihood = {res:-2327.1893}  
Iteration 3:{space 3}log likelihood = {res:-2327.1879}  
Iteration 4:{space 3}log likelihood = {res:-2327.1879}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-2246.9453}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-2246.9453}  (not concave)
Iteration 1:{space 3}log likelihood = {res:-2209.2202}  (not concave)
Iteration 2:{space 3}log likelihood = {res:-2206.5124}  (not concave)
Iteration 3:{space 3}log likelihood = {res:-2205.8008}  (not concave)
Iteration 4:{space 3}log likelihood = {res:-2203.3271}  
Iteration 5:{space 3}log likelihood = {res:-2202.7784}  (not concave)
Iteration 6:{space 3}log likelihood = {res:-2202.7452}  (not concave)
Iteration 7:{space 3}log likelihood = {res:-2202.3925}  (not concave)
Iteration 8:{space 3}log likelihood = {res:-2202.3859}  
Iteration 9:{space 3}log likelihood = {res:-2202.3851}  
Iteration 10:{space 2}log likelihood = {res:-2202.3851}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     6,276

{txt}{hline 16}{c TT}{hline 44}
{col 17}{txt}{c |}{col 23}No. of{col 36}Observations per Group
{col 2}{txt}Group Variable{col 17}{c |}{col 23}Groups{col 33}Minimum{col 44}Average{col 55}Maximum
{txt}{hline 16}{c +}{hline 44}
{col 12}{res}_all{col 17}{txt}{c |}{res}{col 21}       1{col 31}    6,276{col 42}  6,276.0{col 53}    6,276
{col 8}{res}statenum{col 17}{txt}{c |}{res}{col 21}      20{col 31}       98{col 42}    313.8{col 53}      634
{txt}{hline 16}{c BT}{hline 44}

{txt}Integration method: {col 25}{res}laplace

{col 49}{txt}Wald chi2({res}15{txt}){col 67}={res}{col 70}   333.32
{txt}Log likelihood = {res}-2202.3851{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}            polspon{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}oos_adopt_d {c |}{col 21}{res}{space 2} .5263279{col 33}{space 2} .1273498{col 44}{space 1}    4.13{col 53}{space 3}0.000{col 61}{space 4} .2767268{col 74}{space 3} .7759289
{txt}{space 14}judic {c |}{col 21}{res}{space 2} .3243573{col 33}{space 2} .0956175{col 44}{space 1}    3.39{col 53}{space 3}0.001{col 61}{space 4} .1369504{col 74}{space 3} .5117642
{txt}{space 14}chair {c |}{col 21}{res}{space 2} .3128072{col 33}{space 2} .3076124{col 44}{space 1}    1.02{col 53}{space 3}0.309{col 61}{space 4}-.2901021{col 74}{space 3} .9157165
{txt}{space 13}leader {c |}{col 21}{res}{space 2}-.0390825{col 33}{space 2} .1739144{col 44}{space 1}   -0.22{col 53}{space 3}0.822{col 61}{space 4}-.3799484{col 74}{space 3} .3017834
{txt}{space 13}female {c |}{col 21}{res}{space 2} .0951321{col 33}{space 2}  .100396{col 44}{space 1}    0.95{col 53}{space 3}0.343{col 61}{space 4}-.1016405{col 74}{space 3} .2919046
{txt}{space 12}majmemb {c |}{col 21}{res}{space 2} .1963926{col 33}{space 2} .0865357{col 44}{space 1}    2.27{col 53}{space 3}0.023{col 61}{space 4} .0267856{col 74}{space 3} .3659995
{txt}{space 11}blackpct {c |}{col 21}{res}{space 2}-1.338595{col 33}{space 2} .3002915{col 44}{space 1}   -4.46{col 53}{space 3}0.000{col 61}{space 4}-1.927156{col 74}{space 3}-.7500346
{txt}{space 15}srty {c |}{col 21}{res}{space 2}-.0294056{col 33}{space 2} .0067304{col 44}{space 1}   -4.37{col 53}{space 3}0.000{col 61}{space 4} -.042597{col 74}{space 3}-.0162142
{txt}{space 13}senate {c |}{col 21}{res}{space 2} .8345452{col 33}{space 2} .1120632{col 44}{space 1}    7.45{col 53}{space 3}0.000{col 61}{space 4} .6149053{col 74}{space 3} 1.054185
{txt}{space 13}squire {c |}{col 21}{res}{space 2}-3.554497{col 33}{space 2} 3.873558{col 44}{space 1}   -0.92{col 53}{space 3}0.359{col 61}{space 4}-11.14653{col 74}{space 3} 4.037538
{txt}{space 12}sponlim {c |}{col 21}{res}{space 2} .3081207{col 33}{space 2} .3928901{col 44}{space 1}    0.78{col 53}{space 3}0.433{col 61}{space 4}-.4619298{col 74}{space 3} 1.078171
{txt}{space 12}termlim {c |}{col 21}{res}{space 2} 2.684777{col 33}{space 2} .4510462{col 44}{space 1}    5.95{col 53}{space 3}0.000{col 61}{space 4} 1.800742{col 74}{space 3} 3.568811
{txt}{space 12}polcons {c |}{col 21}{res}{space 2} 2.780229{col 33}{space 2} 1.170444{col 44}{space 1}    2.38{col 53}{space 3}0.018{col 61}{space 4}  .486201{col 74}{space 3} 5.074258
{txt}{space 12}smideol {c |}{col 21}{res}{space 2}-2.557807{col 33}{space 2} .2610157{col 44}{space 1}   -9.80{col 53}{space 3}0.000{col 61}{space 4}-3.069388{col 74}{space 3}-2.046226
{txt}{space 19} {c |}
c.polcons#c.smideol {c |}{col 21}{res}{space 2} 5.231767{col 33}{space 2} .4674926{col 44}{space 1}   11.19{col 53}{space 3}0.000{col 61}{space 4} 4.315498{col 74}{space 3} 6.148035
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2}-3.832403{col 33}{space 2} .9362217{col 44}{space 1}   -4.09{col 53}{space 3}0.000{col 61}{space 4}-5.667364{col 74}{space 3}-1.997442
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}_all>polnum        {col 21}{txt}{c |}
{space 10}var(_cons){c |}{col 21}{res}{space 2} .2036076{col 33}{space 2} .1328373{col 61}{space 4} .0566832{col 74}{space 3} .7313636
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}statenum           {col 21}{txt}{c |}
{space 10}var(_cons){c |}{col 21}{res}{space 2} 2.245864{col 33}{space 2} .9363281{col 61}{space 4} .9919898{col 74}{space 3} 5.084635
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}chi2({res}2{txt}) ={res} 249.61{col 59}{txt}Prob > chi2 ={res}{col 73}0.0000

{txt}{p 0 6 4 79}Note: {help j_mixedlr##|_new:LR test is conservative} and provided only for reference.{p_end}

{com}. *Other policies
. quietly melogit polspon oos_adopt_d judic chair leader female majmemb blackpct srty senate squire sponlim termlim c.polcons##c.smideol if purecj==0 || _all: R.polnum
{txt}
{com}. matrix c4 = e(b)
{txt}
{com}. melogit polspon oos_adopt_d judic chair leader female majmemb blackpct srty senate squire sponlim termlim c.polcons##c.smideol if purecj==0 || _all: R.polnum|| statenum:, from(c4) difficult
{res}{txt}{p 0 0 2}
note: crossed random-effects model specified; option intmethod(laplace) implied
{p_end}

Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res: -2880.143}  
Iteration 1:{space 3}log likelihood = {res:-2778.4041}  
Iteration 2:{space 3}log likelihood = {res: -2777.284}  
Iteration 3:{space 3}log likelihood = {res:-2777.2834}  
Iteration 4:{space 3}log likelihood = {res:-2777.2834}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-2587.4005}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-2587.4005}  (not concave)
Iteration 1:{space 3}log likelihood = {res:-2577.4052}  
Iteration 2:{space 3}log likelihood = {res:-2577.2936}  (not concave)
Iteration 3:{space 3}log likelihood = {res:-2577.2569}  
Iteration 4:{space 3}log likelihood = {res:-2577.1234}  
Iteration 5:{space 3}log likelihood = {res:-2577.1086}  (not concave)
Iteration 6:{space 3}log likelihood = {res: -2577.108}  
Iteration 7:{space 3}log likelihood = {res: -2577.108}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     8,772

{txt}{hline 16}{c TT}{hline 44}
{col 17}{txt}{c |}{col 23}No. of{col 36}Observations per Group
{col 2}{txt}Group Variable{col 17}{c |}{col 23}Groups{col 33}Minimum{col 44}Average{col 55}Maximum
{txt}{hline 16}{c +}{hline 44}
{col 12}{res}_all{col 17}{txt}{c |}{res}{col 21}       1{col 31}    8,772{col 42}  8,772.0{col 53}    8,772
{col 8}{res}statenum{col 17}{txt}{c |}{res}{col 21}      20{col 31}      144{col 42}    438.6{col 53}      680
{txt}{hline 16}{c BT}{hline 44}

{txt}Integration method: {col 25}{res}laplace

{col 49}{txt}Wald chi2({res}15{txt}){col 67}={res}{col 70}   358.56
{txt}Log likelihood = {res} -2577.108{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}            polspon{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}oos_adopt_d {c |}{col 21}{res}{space 2} .0281427{col 33}{space 2} .1483362{col 44}{space 1}    0.19{col 53}{space 3}0.850{col 61}{space 4} -.262591{col 74}{space 3} .3188763
{txt}{space 14}judic {c |}{col 21}{res}{space 2}  .546994{col 33}{space 2} .0902365{col 44}{space 1}    6.06{col 53}{space 3}0.000{col 61}{space 4} .3701337{col 74}{space 3} .7238543
{txt}{space 14}chair {c |}{col 21}{res}{space 2} .7946495{col 33}{space 2} .2612131{col 44}{space 1}    3.04{col 53}{space 3}0.002{col 61}{space 4} .2826813{col 74}{space 3} 1.306618
{txt}{space 13}leader {c |}{col 21}{res}{space 2} .2704729{col 33}{space 2} .1296898{col 44}{space 1}    2.09{col 53}{space 3}0.037{col 61}{space 4} .0162856{col 74}{space 3} .5246602
{txt}{space 13}female {c |}{col 21}{res}{space 2}  .031052{col 33}{space 2} .0915822{col 44}{space 1}    0.34{col 53}{space 3}0.735{col 61}{space 4}-.1484458{col 74}{space 3} .2105498
{txt}{space 12}majmemb {c |}{col 21}{res}{space 2} .1416406{col 33}{space 2} .0776354{col 44}{space 1}    1.82{col 53}{space 3}0.068{col 61}{space 4} -.010522{col 74}{space 3} .2938033
{txt}{space 11}blackpct {c |}{col 21}{res}{space 2} .4447712{col 33}{space 2} .2403278{col 44}{space 1}    1.85{col 53}{space 3}0.064{col 61}{space 4}-.0262627{col 74}{space 3} .9158051
{txt}{space 15}srty {c |}{col 21}{res}{space 2}-.0311834{col 33}{space 2} .0065187{col 44}{space 1}   -4.78{col 53}{space 3}0.000{col 61}{space 4}-.0439598{col 74}{space 3} -.018407
{txt}{space 13}senate {c |}{col 21}{res}{space 2} .8024797{col 33}{space 2} .0966139{col 44}{space 1}    8.31{col 53}{space 3}0.000{col 61}{space 4}   .61312{col 74}{space 3} .9918395
{txt}{space 13}squire {c |}{col 21}{res}{space 2} 6.797614{col 33}{space 2} 2.116698{col 44}{space 1}    3.21{col 53}{space 3}0.001{col 61}{space 4} 2.648963{col 74}{space 3} 10.94627
{txt}{space 12}sponlim {c |}{col 21}{res}{space 2}-.0312632{col 33}{space 2} .2625718{col 44}{space 1}   -0.12{col 53}{space 3}0.905{col 61}{space 4}-.5458945{col 74}{space 3} .4833681
{txt}{space 12}termlim {c |}{col 21}{res}{space 2} .0357831{col 33}{space 2} .2805204{col 44}{space 1}    0.13{col 53}{space 3}0.898{col 61}{space 4}-.5140267{col 74}{space 3}  .585593
{txt}{space 12}polcons {c |}{col 21}{res}{space 2} 2.749381{col 33}{space 2} 1.243196{col 44}{space 1}    2.21{col 53}{space 3}0.027{col 61}{space 4} .3127603{col 74}{space 3} 5.186001
{txt}{space 12}smideol {c |}{col 21}{res}{space 2} -1.96866{col 33}{space 2}  .160014{col 44}{space 1}  -12.30{col 53}{space 3}0.000{col 61}{space 4}-2.282282{col 74}{space 3}-1.655038
{txt}{space 19} {c |}
c.polcons#c.smideol {c |}{col 21}{res}{space 2} 4.506278{col 33}{space 2} .3358626{col 44}{space 1}   13.42{col 53}{space 3}0.000{col 61}{space 4}    3.848{col 74}{space 3} 5.164557
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2} -5.28849{col 33}{space 2} .7263373{col 44}{space 1}   -7.28{col 53}{space 3}0.000{col 61}{space 4}-6.712085{col 74}{space 3}-3.864895
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}_all>polnum        {col 21}{txt}{c |}
{space 10}var(_cons){c |}{col 21}{res}{space 2} .2194626{col 33}{space 2}  .141038{col 61}{space 4} .0622779{col 74}{space 3}  .773369
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}statenum           {col 21}{txt}{c |}
{space 10}var(_cons){c |}{col 21}{res}{space 2} .7711663{col 33}{space 2} .2747296{col 61}{space 4} .3836239{col 74}{space 3}  1.55021
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}chi2({res}2{txt}) ={res} 400.35{col 59}{txt}Prob > chi2 ={res}{col 73}0.0000

{txt}{p 0 6 4 79}Note: {help j_mixedlr##|_new:LR test is conservative} and provided only for reference.{p_end}

{com}. 
. *Party similarity of legislature vs. previous adopting legislator vs. sponsor (Main text)
. ttest polauth if oos_adopt_d==1, by(adoptleg_same)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
      No {c |}{res}{col 12}    255{col 22} .1098039{col 34} .0196171{col 46}   .31326{col 58}  .071171{col 70} .1484368
     {txt}Yes {c |}{res}{col 12}    773{col 22}  .068564{col 34} .0090953{col 46} .2528749{col 58} .0507096{col 70} .0864184
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}  1,028{col 22} .0787938{col 34}  .008407{col 46} .2695478{col 58}  .062297{col 70} .0952906
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} .0412399{col 34} .0194327{col 58} .0031075{col 70} .0793722
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}No{txt}) - mean({res}Yes{txt})                                   t = {res}  2.1222
{txt}Ho: diff = 0                                     degrees of freedom = {res}    1026

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.9830         {txt}Pr(|T| > |t|) = {res}0.0341          {txt}Pr(T > t) = {res}0.0170
{txt}
{com}. ttest polspon if oos_adopt_d==1, by(adoptleg_same)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
      No {c |}{res}{col 12}    255{col 22} .2235294{col 34} .0261404{col 46} .4174294{col 58} .1720498{col 70}  .275009
     {txt}Yes {c |}{res}{col 12}    773{col 22} .1617076{col 34} .0132512{col 46} .3684208{col 58}  .135695{col 70} .1877202
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}  1,028{col 22} .1770428{col 34} .0119108{col 46} .3818907{col 58} .1536704{col 70} .2004152
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} .0618218{col 34} .0275247{col 58} .0078107{col 70} .1158329
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}No{txt}) - mean({res}Yes{txt})                                   t = {res}  2.2460
{txt}Ho: diff = 0                                     degrees of freedom = {res}    1026

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.9875         {txt}Pr(|T| > |t|) = {res}0.0249          {txt}Pr(T > t) = {res}0.0125
{txt}
{com}. 
. *Split models by party (Main text and Table A-5, Rows 4 and 5) 
. *Authorship models
. *Republicans
. quietly melogit polauth oos_adopt_d judic chair leader female majmemb blackpct srty senate squire introlim termlim c.polcons##c.smideol if multauth==1 & party==200 || _all: R.polnum
{txt}
{com}. matrix k1 = e(b)
{txt}
{com}. melogit polauth oos_adopt_d judic chair leader female majmemb blackpct srty senate squire introlim termlim c.polcons##c.smideol if multauth==1 & party==200 || _all: R.polnum|| statenum:, from(k1) difficult
{res}{txt}{p 0 0 2}
note: crossed random-effects model specified; option intmethod(laplace) implied
{p_end}

Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-1355.6734}  
Iteration 1:{space 3}log likelihood = {res:-1194.8523}  
Iteration 2:{space 3}log likelihood = {res:-1189.1348}  
Iteration 3:{space 3}log likelihood = {res:-1189.0944}  
Iteration 4:{space 3}log likelihood = {res:-1189.0943}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-1135.8901}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-1135.8901}  
Iteration 1:{space 3}log likelihood = {res:-1130.0169}  (not concave)
Iteration 2:{space 3}log likelihood = {res:-1126.4872}  (not concave)
Iteration 3:{space 3}log likelihood = {res:-1126.2186}  (not concave)
Iteration 4:{space 3}log likelihood = {res:-1114.1325}  (not concave)
Iteration 5:{space 3}log likelihood = {res:-1114.1294}  (not concave)
Iteration 6:{space 3}log likelihood = {res:-1113.8031}  (not concave)
Iteration 7:{space 3}log likelihood = {res:-1113.7568}  (not concave)
Iteration 8:{space 3}log likelihood = {res:-1113.6205}  (not concave)
Iteration 9:{space 3}log likelihood = {res:-1113.5473}  (not concave)
Iteration 10:{space 2}log likelihood = {res:-1113.5457}  (not concave)
Iteration 11:{space 2}log likelihood = {res:-1113.5448}  (not concave)
Iteration 12:{space 2}log likelihood = {res:-1113.5164}  (not concave)
Iteration 13:{space 2}log likelihood = {res:-1113.5105}  (not concave)
Iteration 14:{space 2}log likelihood = {res:-1113.5083}  
Iteration 15:{space 2}log likelihood = {res:-1113.5082}  
Iteration 16:{space 2}log likelihood = {res:-1113.5082}  
Iteration 17:{space 2}log likelihood = {res:-1113.5082}  
Iteration 18:{space 2}log likelihood = {res:-1113.5082}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     4,868

{txt}{hline 16}{c TT}{hline 44}
{col 17}{txt}{c |}{col 23}No. of{col 36}Observations per Group
{col 2}{txt}Group Variable{col 17}{c |}{col 23}Groups{col 33}Minimum{col 44}Average{col 55}Maximum
{txt}{hline 16}{c +}{hline 44}
{col 12}{res}_all{col 17}{txt}{c |}{res}{col 21}       1{col 31}    4,868{col 42}  4,868.0{col 53}    4,868
{col 8}{res}statenum{col 17}{txt}{c |}{res}{col 21}      14{col 31}      143{col 42}    347.7{col 53}      622
{txt}{hline 16}{c BT}{hline 44}

{txt}Integration method: {col 25}{res}laplace

{col 49}{txt}Wald chi2({res}15{txt}){col 67}={res}{col 70}   143.87
{txt}Log likelihood = {res}-1113.5082{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}            polauth{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}oos_adopt_d {c |}{col 21}{res}{space 2} .4184327{col 33}{space 2} .2341704{col 44}{space 1}    1.79{col 53}{space 3}0.074{col 61}{space 4}-.0405328{col 74}{space 3} .8773983
{txt}{space 14}judic {c |}{col 21}{res}{space 2} .3926119{col 33}{space 2} .1430466{col 44}{space 1}    2.74{col 53}{space 3}0.006{col 61}{space 4} .1122457{col 74}{space 3} .6729782
{txt}{space 14}chair {c |}{col 21}{res}{space 2} 1.187556{col 33}{space 2} .3544887{col 44}{space 1}    3.35{col 53}{space 3}0.001{col 61}{space 4} .4927705{col 74}{space 3}  1.88234
{txt}{space 13}leader {c |}{col 21}{res}{space 2}-.0464299{col 33}{space 2} .2265948{col 44}{space 1}   -0.20{col 53}{space 3}0.838{col 61}{space 4}-.4905476{col 74}{space 3} .3976877
{txt}{space 13}female {c |}{col 21}{res}{space 2}   .24186{col 33}{space 2} .1389071{col 44}{space 1}    1.74{col 53}{space 3}0.082{col 61}{space 4} -.030393{col 74}{space 3}  .514113
{txt}{space 12}majmemb {c |}{col 21}{res}{space 2} .7104874{col 33}{space 2} .2001884{col 44}{space 1}    3.55{col 53}{space 3}0.000{col 61}{space 4} .3181254{col 74}{space 3}  1.10285
{txt}{space 11}blackpct {c |}{col 21}{res}{space 2}-.5455497{col 33}{space 2} 1.156555{col 44}{space 1}   -0.47{col 53}{space 3}0.637{col 61}{space 4}-2.812355{col 74}{space 3} 1.721256
{txt}{space 15}srty {c |}{col 21}{res}{space 2}-.0172334{col 33}{space 2}  .012358{col 44}{space 1}   -1.39{col 53}{space 3}0.163{col 61}{space 4}-.0414546{col 74}{space 3} .0069879
{txt}{space 13}senate {c |}{col 21}{res}{space 2} 1.130374{col 33}{space 2} .1561463{col 44}{space 1}    7.24{col 53}{space 3}0.000{col 61}{space 4} .8243332{col 74}{space 3} 1.436415
{txt}{space 13}squire {c |}{col 21}{res}{space 2}  3.49957{col 33}{space 2} 2.555294{col 44}{space 1}    1.37{col 53}{space 3}0.171{col 61}{space 4}-1.508715{col 74}{space 3} 8.507855
{txt}{space 11}introlim {c |}{col 21}{res}{space 2} -1.36522{col 33}{space 2} .7743411{col 44}{space 1}   -1.76{col 53}{space 3}0.078{col 61}{space 4}  -2.8829{col 74}{space 3} .1524609
{txt}{space 12}termlim {c |}{col 21}{res}{space 2} .6958751{col 33}{space 2}   .30578{col 44}{space 1}    2.28{col 53}{space 3}0.023{col 61}{space 4} .0965573{col 74}{space 3} 1.295193
{txt}{space 12}polcons {c |}{col 21}{res}{space 2} 2.472738{col 33}{space 2} 1.767139{col 44}{space 1}    1.40{col 53}{space 3}0.162{col 61}{space 4}-.9907919{col 74}{space 3} 5.936268
{txt}{space 12}smideol {c |}{col 21}{res}{space 2}-2.216067{col 33}{space 2} .9981882{col 44}{space 1}   -2.22{col 53}{space 3}0.026{col 61}{space 4} -4.17248{col 74}{space 3}-.2596539
{txt}{space 19} {c |}
c.polcons#c.smideol {c |}{col 21}{res}{space 2} 5.136093{col 33}{space 2} 1.821046{col 44}{space 1}    2.82{col 53}{space 3}0.005{col 61}{space 4} 1.566908{col 74}{space 3} 8.705278
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2}-5.897149{col 33}{space 2} 1.079948{col 44}{space 1}   -5.46{col 53}{space 3}0.000{col 61}{space 4}-8.013809{col 74}{space 3}-3.780489
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}_all>polnum        {col 21}{txt}{c |}
{space 10}var(_cons){c |}{col 21}{res}{space 2} .3456747{col 33}{space 2} .1905972{col 61}{space 4} .1173099{col 74}{space 3} 1.018593
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}statenum           {col 21}{txt}{c |}
{space 10}var(_cons){c |}{col 21}{res}{space 2} .7878321{col 33}{space 2} .3594101{col 61}{space 4} .3221917{col 74}{space 3} 1.926429
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}chi2({res}2{txt}) ={res} 151.17{col 59}{txt}Prob > chi2 ={res}{col 73}0.0000

{txt}{p 0 6 4 79}Note: {help j_mixedlr##|_new:LR test is conservative} and provided only for reference.{p_end}

{com}. margins, dydx(oos_adopt_d) post
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     4,868
{txt}{col 1}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:oos_adopt_d}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}oos_adopt_d {c |}{col 14}{res}{space 2} .0253461{col 26}{space 2} .0148516{col 37}{space 1}    1.71{col 46}{space 3}0.088{col 54}{space 4}-.0037625{col 67}{space 3} .0544547
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. est store rep_auth
{txt}
{com}. *Democrats
. quietly melogit polauth oos_adopt_d judic chair leader female majmemb blackpct srty senate squire introlim termlim c.polcons##c.smideol if multauth==1 & party==100 || _all: R.polnum
{txt}
{com}. matrix k2 = e(b)
{txt}
{com}. melogit polauth oos_adopt_d judic chair leader female majmemb blackpct srty senate squire introlim termlim c.polcons##c.smideol if multauth==1 & party==100 || _all: R.polnum|| statenum:, from(k2) difficult
{res}{txt}{p 0 0 2}
note: crossed random-effects model specified; option intmethod(laplace) implied
{p_end}

Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res: -1417.352}  
Iteration 1:{space 3}log likelihood = {res: -1294.724}  
Iteration 2:{space 3}log likelihood = {res:-1291.7497}  
Iteration 3:{space 3}log likelihood = {res:-1291.7313}  
Iteration 4:{space 3}log likelihood = {res:-1291.7313}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-1242.2675}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-1242.2675}  (not concave)
Iteration 1:{space 3}log likelihood = {res:-1238.3918}  
Iteration 2:{space 3}log likelihood = {res:-1237.6364}  (not concave)
Iteration 3:{space 3}log likelihood = {res:-1237.5599}  (not concave)
Iteration 4:{space 3}log likelihood = {res:-1237.3417}  (not concave)
Iteration 5:{space 3}log likelihood = {res:-1237.3271}  (not concave)
Iteration 6:{space 3}log likelihood = {res:-1237.3231}  
Iteration 7:{space 3}log likelihood = {res:-1237.3223}  
Iteration 8:{space 3}log likelihood = {res:-1237.3223}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     5,894

{txt}{hline 16}{c TT}{hline 44}
{col 17}{txt}{c |}{col 23}No. of{col 36}Observations per Group
{col 2}{txt}Group Variable{col 17}{c |}{col 23}Groups{col 33}Minimum{col 44}Average{col 55}Maximum
{txt}{hline 16}{c +}{hline 44}
{col 12}{res}_all{col 17}{txt}{c |}{res}{col 21}       1{col 31}    5,894{col 42}  5,894.0{col 53}    5,894
{col 8}{res}statenum{col 17}{txt}{c |}{res}{col 21}      14{col 31}      122{col 42}    421.0{col 53}      807
{txt}{hline 16}{c BT}{hline 44}

{txt}Integration method: {col 25}{res}laplace

{col 49}{txt}Wald chi2({res}15{txt}){col 67}={res}{col 70}   102.97
{txt}Log likelihood = {res}-1237.3223{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}            polauth{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}oos_adopt_d {c |}{col 21}{res}{space 2}   .26523{col 33}{space 2}  .179507{col 44}{space 1}    1.48{col 53}{space 3}0.140{col 61}{space 4}-.0865973{col 74}{space 3} .6170573
{txt}{space 14}judic {c |}{col 21}{res}{space 2} .7612502{col 33}{space 2} .1331096{col 44}{space 1}    5.72{col 53}{space 3}0.000{col 61}{space 4} .5003602{col 74}{space 3}  1.02214
{txt}{space 14}chair {c |}{col 21}{res}{space 2} .2758288{col 33}{space 2} .4203823{col 44}{space 1}    0.66{col 53}{space 3}0.512{col 61}{space 4}-.5481054{col 74}{space 3} 1.099763
{txt}{space 13}leader {c |}{col 21}{res}{space 2} .3199322{col 33}{space 2} .2094205{col 44}{space 1}    1.53{col 53}{space 3}0.127{col 61}{space 4}-.0905244{col 74}{space 3} .7303888
{txt}{space 13}female {c |}{col 21}{res}{space 2} .0447686{col 33}{space 2} .1395611{col 44}{space 1}    0.32{col 53}{space 3}0.748{col 61}{space 4}-.2287662{col 74}{space 3} .3183034
{txt}{space 12}majmemb {c |}{col 21}{res}{space 2}-.2282639{col 33}{space 2} .1887784{col 44}{space 1}   -1.21{col 53}{space 3}0.227{col 61}{space 4}-.5982627{col 74}{space 3} .1417348
{txt}{space 11}blackpct {c |}{col 21}{res}{space 2} .1390585{col 33}{space 2} .3251535{col 44}{space 1}    0.43{col 53}{space 3}0.669{col 61}{space 4}-.4982305{col 74}{space 3} .7763476
{txt}{space 15}srty {c |}{col 21}{res}{space 2}-.0153462{col 33}{space 2} .0082057{col 44}{space 1}   -1.87{col 53}{space 3}0.061{col 61}{space 4} -.031429{col 74}{space 3} .0007366
{txt}{space 13}senate {c |}{col 21}{res}{space 2} .2265947{col 33}{space 2}  .150404{col 44}{space 1}    1.51{col 53}{space 3}0.132{col 61}{space 4}-.0681917{col 74}{space 3} .5213811
{txt}{space 13}squire {c |}{col 21}{res}{space 2} 3.293158{col 33}{space 2} 2.166623{col 44}{space 1}    1.52{col 53}{space 3}0.129{col 61}{space 4}-.9533448{col 74}{space 3} 7.539661
{txt}{space 11}introlim {c |}{col 21}{res}{space 2} -.907238{col 33}{space 2} .5658866{col 44}{space 1}   -1.60{col 53}{space 3}0.109{col 61}{space 4}-2.016355{col 74}{space 3} .2018794
{txt}{space 12}termlim {c |}{col 21}{res}{space 2}-.3384572{col 33}{space 2}  .360969{col 44}{space 1}   -0.94{col 53}{space 3}0.348{col 61}{space 4}-1.045943{col 74}{space 3}  .369029
{txt}{space 12}polcons {c |}{col 21}{res}{space 2} 2.021785{col 33}{space 2} 1.368195{col 44}{space 1}    1.48{col 53}{space 3}0.139{col 61}{space 4} -.659828{col 74}{space 3} 4.703397
{txt}{space 12}smideol {c |}{col 21}{res}{space 2}-2.225294{col 33}{space 2} .3842407{col 44}{space 1}   -5.79{col 53}{space 3}0.000{col 61}{space 4}-2.978392{col 74}{space 3}-1.472196
{txt}{space 19} {c |}
c.polcons#c.smideol {c |}{col 21}{res}{space 2} 5.144091{col 33}{space 2} .7796525{col 44}{space 1}    6.60{col 53}{space 3}0.000{col 61}{space 4}    3.616{col 74}{space 3} 6.672181
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2}-4.511999{col 33}{space 2} .9692552{col 44}{space 1}   -4.66{col 53}{space 3}0.000{col 61}{space 4}-6.411705{col 74}{space 3}-2.612294
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}_all>polnum        {col 21}{txt}{c |}
{space 10}var(_cons){c |}{col 21}{res}{space 2} .2691032{col 33}{space 2} .1404632{col 61}{space 4} .0967429{col 74}{space 3} .7485462
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}statenum           {col 21}{txt}{c |}
{space 10}var(_cons){c |}{col 21}{res}{space 2} .4026709{col 33}{space 2} .1807104{col 61}{space 4} .1670893{col 74}{space 3} .9704024
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}chi2({res}2{txt}) ={res} 108.82{col 59}{txt}Prob > chi2 ={res}{col 73}0.0000

{txt}{p 0 6 4 79}Note: {help j_mixedlr##|_new:LR test is conservative} and provided only for reference.{p_end}

{com}. margins, dydx(oos_adopt_d) post
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     5,894
{txt}{col 1}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:oos_adopt_d}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}oos_adopt_d {c |}{col 14}{res}{space 2} .0132281{col 26}{space 2} .0091648{col 37}{space 1}    1.44{col 46}{space 3}0.149{col 54}{space 4}-.0047346{col 67}{space 3} .0311908
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. est store dem_auth
{txt}
{com}. *Sponsorship Models
. *Republicans
. quietly melogit polspon oos_adopt_d judic chair leader female majmemb blackpct srty senate squire sponlim termlim c.polcons##c.smideol if party==200 || _all: R.polnum
{txt}
{com}. matrix l1 = e(b)
{txt}
{com}. melogit polspon oos_adopt_d judic chair leader female majmemb blackpct srty senate squire sponlim termlim c.polcons##c.smideol if party==200 || _all: R.polnum|| statenum:, from(l1) difficult
{res}{txt}{p 0 0 2}
note: crossed random-effects model specified; option intmethod(laplace) implied
{p_end}

Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-2433.6505}  
Iteration 1:{space 3}log likelihood = {res:-2296.9988}  
Iteration 2:{space 3}log likelihood = {res:-2293.3745}  
Iteration 3:{space 3}log likelihood = {res: -2293.362}  
Iteration 4:{space 3}log likelihood = {res: -2293.362}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-2184.6449}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-2184.6449}  (not concave)
Iteration 1:{space 3}log likelihood = {res:-2170.0473}  (not concave)
Iteration 2:{space 3}log likelihood = {res:-2159.8936}  (not concave)
Iteration 3:{space 3}log likelihood = {res:-2157.6735}  (not concave)
Iteration 4:{space 3}log likelihood = {res:-2153.3277}  (not concave)
Iteration 5:{space 3}log likelihood = {res:-2152.7107}  (not concave)
Iteration 6:{space 3}log likelihood = {res:-2150.2214}  (not concave)
Iteration 7:{space 3}log likelihood = {res:-2149.9512}  (not concave)
Iteration 8:{space 3}log likelihood = {res:-2149.8312}  
Iteration 9:{space 3}log likelihood = {res:-2149.1738}  (not concave)
Iteration 10:{space 2}log likelihood = {res: -2149.151}  (not concave)
Iteration 11:{space 2}log likelihood = {res:-2149.0632}  
Iteration 12:{space 2}log likelihood = {res:-2149.0499}  
Iteration 13:{space 2}log likelihood = {res:-2149.0136}  (not concave)
Iteration 14:{space 2}log likelihood = {res:-2148.9185}  (not concave)
Iteration 15:{space 2}log likelihood = {res:-2148.8984}  
Iteration 16:{space 2}log likelihood = {res:-2148.8895}  
Iteration 17:{space 2}log likelihood = {res:-2148.8855}  
Iteration 18:{space 2}log likelihood = {res:-2148.8764}  
Iteration 19:{space 2}log likelihood = {res:-2148.8756}  
Iteration 20:{space 2}log likelihood = {res:-2148.8755}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     6,724

{txt}{hline 16}{c TT}{hline 44}
{col 17}{txt}{c |}{col 23}No. of{col 36}Observations per Group
{col 2}{txt}Group Variable{col 17}{c |}{col 23}Groups{col 33}Minimum{col 44}Average{col 55}Maximum
{txt}{hline 16}{c +}{hline 44}
{col 12}{res}_all{col 17}{txt}{c |}{res}{col 21}       1{col 31}    6,724{col 42}  6,724.0{col 53}    6,724
{col 8}{res}statenum{col 17}{txt}{c |}{res}{col 21}      20{col 31}      115{col 42}    336.2{col 53}      622
{txt}{hline 16}{c BT}{hline 44}

{txt}Integration method: {col 25}{res}laplace

{col 49}{txt}Wald chi2({res}15{txt}){col 67}={res}{col 70}   282.37
{txt}Log likelihood = {res}-2148.8755{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}            polspon{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}oos_adopt_d {c |}{col 21}{res}{space 2}  .400999{col 33}{space 2} .1575535{col 44}{space 1}    2.55{col 53}{space 3}0.011{col 61}{space 4} .0921998{col 74}{space 3} .7097982
{txt}{space 14}judic {c |}{col 21}{res}{space 2} .3392505{col 33}{space 2} .0982742{col 44}{space 1}    3.45{col 53}{space 3}0.001{col 61}{space 4} .1466367{col 74}{space 3} .5318644
{txt}{space 14}chair {c |}{col 21}{res}{space 2}  .880785{col 33}{space 2} .2893664{col 44}{space 1}    3.04{col 53}{space 3}0.002{col 61}{space 4} .3136373{col 74}{space 3} 1.447933
{txt}{space 13}leader {c |}{col 21}{res}{space 2} .0419062{col 33}{space 2} .1633142{col 44}{space 1}    0.26{col 53}{space 3}0.797{col 61}{space 4}-.2781838{col 74}{space 3} .3619962
{txt}{space 13}female {c |}{col 21}{res}{space 2} .0717077{col 33}{space 2}  .104346{col 44}{space 1}    0.69{col 53}{space 3}0.492{col 61}{space 4}-.1328067{col 74}{space 3}  .276222
{txt}{space 12}majmemb {c |}{col 21}{res}{space 2} .6732943{col 33}{space 2} .1319125{col 44}{space 1}    5.10{col 53}{space 3}0.000{col 61}{space 4} .4147505{col 74}{space 3} .9318381
{txt}{space 11}blackpct {c |}{col 21}{res}{space 2}-.2312352{col 33}{space 2}  .723293{col 44}{space 1}   -0.32{col 53}{space 3}0.749{col 61}{space 4}-1.648863{col 74}{space 3} 1.186393
{txt}{space 15}srty {c |}{col 21}{res}{space 2}-.0451659{col 33}{space 2}   .00848{col 44}{space 1}   -5.33{col 53}{space 3}0.000{col 61}{space 4}-.0617864{col 74}{space 3}-.0285453
{txt}{space 13}senate {c |}{col 21}{res}{space 2} 1.277381{col 33}{space 2} .1135046{col 44}{space 1}   11.25{col 53}{space 3}0.000{col 61}{space 4} 1.054916{col 74}{space 3} 1.499846
{txt}{space 13}squire {c |}{col 21}{res}{space 2} 2.110052{col 33}{space 2} 2.317211{col 44}{space 1}    0.91{col 53}{space 3}0.363{col 61}{space 4}-2.431598{col 74}{space 3} 6.651702
{txt}{space 12}sponlim {c |}{col 21}{res}{space 2}  .857321{col 33}{space 2} .3533514{col 44}{space 1}    2.43{col 53}{space 3}0.015{col 61}{space 4}  .164765{col 74}{space 3} 1.549877
{txt}{space 12}termlim {c |}{col 21}{res}{space 2} .8632219{col 33}{space 2} .2927735{col 44}{space 1}    2.95{col 53}{space 3}0.003{col 61}{space 4} .2893964{col 74}{space 3} 1.437047
{txt}{space 12}polcons {c |}{col 21}{res}{space 2} 1.312038{col 33}{space 2} 1.168931{col 44}{space 1}    1.12{col 53}{space 3}0.262{col 61}{space 4} -.979025{col 74}{space 3}   3.6031
{txt}{space 12}smideol {c |}{col 21}{res}{space 2}-3.269874{col 33}{space 2} .5411335{col 44}{space 1}   -6.04{col 53}{space 3}0.000{col 61}{space 4}-4.330476{col 74}{space 3}-2.209272
{txt}{space 19} {c |}
c.polcons#c.smideol {c |}{col 21}{res}{space 2} 6.777784{col 33}{space 2} .9867694{col 44}{space 1}    6.87{col 53}{space 3}0.000{col 61}{space 4} 4.843752{col 74}{space 3} 8.711817
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2}-4.276254{col 33}{space 2} .7728059{col 44}{space 1}   -5.53{col 53}{space 3}0.000{col 61}{space 4}-5.790926{col 74}{space 3}-2.761583
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}_all>polnum        {col 21}{txt}{c |}
{space 10}var(_cons){c |}{col 21}{res}{space 2} .2743099{col 33}{space 2} .1344766{col 61}{space 4} .1049426{col 74}{space 3} .7170199
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}statenum           {col 21}{txt}{c |}
{space 10}var(_cons){c |}{col 21}{res}{space 2} 1.200113{col 33}{space 2} .4392659{col 61}{space 4} .5856858{col 74}{space 3} 2.459117
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}chi2({res}2{txt}) ={res} 288.97{col 59}{txt}Prob > chi2 ={res}{col 73}0.0000

{txt}{p 0 6 4 79}Note: {help j_mixedlr##|_new:LR test is conservative} and provided only for reference.{p_end}

{com}. margins, dydx(oos_adopt_d) post
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     6,724
{txt}{col 1}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:oos_adopt_d}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}oos_adopt_d {c |}{col 14}{res}{space 2} .0374351{col 26}{space 2} .0154082{col 37}{space 1}    2.43{col 46}{space 3}0.015{col 54}{space 4} .0072356{col 67}{space 3} .0676346
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. est store rep_spon
{txt}
{com}. *Democrats
. quietly melogit polspon oos_adopt_d judic chair leader female majmemb blackpct srty senate squire sponlim termlim c.polcons##c.smideol if party==100 || _all: R.polnum
{txt}
{com}. matrix l2 = e(b)
{txt}
{com}. melogit polspon oos_adopt_d judic chair leader female majmemb blackpct srty senate squire sponlim termlim c.polcons##c.smideol if party==100 || _all: R.polnum|| statenum:, from(l2) difficult
{res}{txt}{p 0 0 2}
note: crossed random-effects model specified; option intmethod(laplace) implied
{p_end}

Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-2898.3335}  
Iteration 1:{space 3}log likelihood = {res:-2842.1773}  
Iteration 2:{space 3}log likelihood = {res:-2841.1587}  
Iteration 3:{space 3}log likelihood = {res:-2841.1583}  
Iteration 4:{space 3}log likelihood = {res:-2841.1583}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-2734.6985}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-2734.6985}  (not concave)
Iteration 1:{space 3}log likelihood = {res:-2722.4301}  
Iteration 2:{space 3}log likelihood = {res:-2722.2419}  
Iteration 3:{space 3}log likelihood = {res:-2722.1565}  (not concave)
Iteration 4:{space 3}log likelihood = {res:-2722.1358}  (not concave)
Iteration 5:{space 3}log likelihood = {res:-2722.1013}  
Iteration 6:{space 3}log likelihood = {res:-2722.0839}  
Iteration 7:{space 3}log likelihood = {res:-2722.0793}  
Iteration 8:{space 3}log likelihood = {res:-2722.0764}  
Iteration 9:{space 3}log likelihood = {res:-2722.0762}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     8,232

{txt}{hline 16}{c TT}{hline 44}
{col 17}{txt}{c |}{col 23}No. of{col 36}Observations per Group
{col 2}{txt}Group Variable{col 17}{c |}{col 23}Groups{col 33}Minimum{col 44}Average{col 55}Maximum
{txt}{hline 16}{c +}{hline 44}
{col 12}{res}_all{col 17}{txt}{c |}{res}{col 21}       1{col 31}    8,232{col 42}  8,232.0{col 53}    8,232
{col 8}{res}statenum{col 17}{txt}{c |}{res}{col 21}      20{col 31}      122{col 42}    411.6{col 53}      807
{txt}{hline 16}{c BT}{hline 44}

{txt}Integration method: {col 25}{res}laplace

{col 49}{txt}Wald chi2({res}15{txt}){col 67}={res}{col 70}   186.38
{txt}Log likelihood = {res}-2722.0762{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}            polspon{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}oos_adopt_d {c |}{col 21}{res}{space 2} .2548127{col 33}{space 2} .1183955{col 44}{space 1}    2.15{col 53}{space 3}0.031{col 61}{space 4} .0227619{col 74}{space 3} .4868636
{txt}{space 14}judic {c |}{col 21}{res}{space 2} .5618488{col 33}{space 2} .0869409{col 44}{space 1}    6.46{col 53}{space 3}0.000{col 61}{space 4} .3914477{col 74}{space 3} .7322499
{txt}{space 14}chair {c |}{col 21}{res}{space 2} .2196062{col 33}{space 2} .2749556{col 44}{space 1}    0.80{col 53}{space 3}0.424{col 61}{space 4}-.3192968{col 74}{space 3} .7585092
{txt}{space 13}leader {c |}{col 21}{res}{space 2} .2956756{col 33}{space 2} .1341109{col 44}{space 1}    2.20{col 53}{space 3}0.027{col 61}{space 4}  .032823{col 74}{space 3} .5585282
{txt}{space 13}female {c |}{col 21}{res}{space 2} .0449632{col 33}{space 2} .0896587{col 44}{space 1}    0.50{col 53}{space 3}0.616{col 61}{space 4}-.1307646{col 74}{space 3} .2206909
{txt}{space 12}majmemb {c |}{col 21}{res}{space 2} .0360759{col 33}{space 2} .1149322{col 44}{space 1}    0.31{col 53}{space 3}0.754{col 61}{space 4}-.1891869{col 74}{space 3} .2613388
{txt}{space 11}blackpct {c |}{col 21}{res}{space 2}-.2357378{col 33}{space 2} .2002414{col 44}{space 1}   -1.18{col 53}{space 3}0.239{col 61}{space 4}-.6282037{col 74}{space 3} .1567282
{txt}{space 15}srty {c |}{col 21}{res}{space 2}-.0233977{col 33}{space 2} .0055296{col 44}{space 1}   -4.23{col 53}{space 3}0.000{col 61}{space 4}-.0342355{col 74}{space 3}  -.01256
{txt}{space 13}senate {c |}{col 21}{res}{space 2} .4454885{col 33}{space 2} .0967618{col 44}{space 1}    4.60{col 53}{space 3}0.000{col 61}{space 4} .2558389{col 74}{space 3}  .635138
{txt}{space 13}squire {c |}{col 21}{res}{space 2} 5.800743{col 33}{space 2} 1.710946{col 44}{space 1}    3.39{col 53}{space 3}0.001{col 61}{space 4}  2.44735{col 74}{space 3} 9.154136
{txt}{space 12}sponlim {c |}{col 21}{res}{space 2}-.2950392{col 33}{space 2} .2823646{col 44}{space 1}   -1.04{col 53}{space 3}0.296{col 61}{space 4}-.8484636{col 74}{space 3} .2583852
{txt}{space 12}termlim {c |}{col 21}{res}{space 2} .0713886{col 33}{space 2} .2763644{col 44}{space 1}    0.26{col 53}{space 3}0.796{col 61}{space 4}-.4702757{col 74}{space 3} .6130529
{txt}{space 12}polcons {c |}{col 21}{res}{space 2} 1.964904{col 33}{space 2} .9030353{col 44}{space 1}    2.18{col 53}{space 3}0.030{col 61}{space 4} .1949873{col 74}{space 3} 3.734821
{txt}{space 12}smideol {c |}{col 21}{res}{space 2}-1.612626{col 33}{space 2} .2357546{col 44}{space 1}   -6.84{col 53}{space 3}0.000{col 61}{space 4}-2.074696{col 74}{space 3}-1.150555
{txt}{space 19} {c |}
c.polcons#c.smideol {c |}{col 21}{res}{space 2} 3.960881{col 33}{space 2} .4649315{col 44}{space 1}    8.52{col 53}{space 3}0.000{col 61}{space 4} 3.049632{col 74}{space 3}  4.87213
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2} -4.19736{col 33}{space 2} .5723364{col 44}{space 1}   -7.33{col 53}{space 3}0.000{col 61}{space 4}-5.319119{col 74}{space 3}-3.075601
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}_all>polnum        {col 21}{txt}{c |}
{space 10}var(_cons){c |}{col 21}{res}{space 2} .2096465{col 33}{space 2} .1007376{col 61}{space 4} .0817476{col 74}{space 3} .5376502
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}statenum           {col 21}{txt}{c |}
{space 10}var(_cons){c |}{col 21}{res}{space 2}  .429358{col 33}{space 2} .1711759{col 61}{space 4} .1965457{col 74}{space 3} .9379411
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}chi2({res}2{txt}) ={res} 238.16{col 59}{txt}Prob > chi2 ={res}{col 73}0.0000

{txt}{p 0 6 4 79}Note: {help j_mixedlr##|_new:LR test is conservative} and provided only for reference.{p_end}

{com}. margins, dydx(oos_adopt_d) post
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     8,232
{txt}{col 1}Model VCE{col 14}: {res}OIM

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Marginal predicted mean, predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:oos_adopt_d}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}oos_adopt_d {c |}{col 14}{res}{space 2} .0228686{col 26}{space 2}  .010987{col 37}{space 1}    2.08{col 46}{space 3}0.037{col 54}{space 4} .0013345{col 67}{space 3} .0444026
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. est store dem_spon
{txt}
{com}. 
. *Figure 1
. coefplot all_auth dem_auth rep_auth || all_spon dem_spon rep_spon, xline(0)
{res}{txt}
{com}. gr_edit .style.editstyle declared_ysize(2.5) editcopy
{res}{txt}
{com}. gr_edit .plotregion1.plotregion1[1]._xylines[1].style.editstyle linestyle(color(black)) editcopy
{res}{txt}
{com}. gr_edit .plotregion1.plotregion1[2]._xylines[1].style.editstyle linestyle(color(black)) editcopy
{res}{txt}
{com}. gr_edit .legend.plotregion1.key[1].view.style.editstyle marker(fillcolor(black)) editcopy
{res}{txt}
{com}. gr_edit .legend.plotregion1.key[1].view.style.editstyle marker(linestyle(color(black))) editcopy
{res}{txt}
{com}. gr_edit .legend.plotregion1.key[2].view.style.editstyle marker(symbol(square)) editcopy
{res}{txt}
{com}. gr_edit .legend.plotregion1.key[2].view.style.editstyle marker(fillcolor(black)) editcopy
{res}{txt}
{com}. gr_edit .legend.plotregion1.key[2].view.style.editstyle marker(linestyle(color(black))) editcopy
{res}{txt}
{com}. gr_edit .legend.plotregion1.key[3].view.style.editstyle marker(symbol(triangle)) editcopy
{res}{txt}
{com}. gr_edit .legend.plotregion1.key[3].view.style.editstyle marker(fillcolor(black)) editcopy
{res}{txt}
{com}. gr_edit .legend.plotregion1.key[3].view.style.editstyle marker(linestyle(color(black))) editcopy
{res}{txt}
{com}. gr_edit .plotregion1.plotregion1[1].plot5.style.editstyle area(linestyle(color(black))) editcopy
{res}{txt}
{com}. gr_edit .plotregion1.plotregion1[1].plot3.style.editstyle area(linestyle(color(black))) editcopy
{res}{txt}
{com}. gr_edit .plotregion1.plotregion1[1].plot1.style.editstyle area(linestyle(color(black))) editcopy
{res}{txt}
{com}. gr_edit .legend.plotregion1.label[1].text = {c -(}{c )-}
{res}{txt}
{com}. gr_edit .legend.plotregion1.label[1].text.Arrpush All Legislators
{res}{txt}
{com}. gr_edit .legend.plotregion1.label[2].text = {c -(}{c )-}
{res}{txt}
{com}. gr_edit .legend.plotregion1.label[2].text.Arrpush Democrats
{res}{txt}
{com}. gr_edit .legend.plotregion1.label[3].text = {c -(}{c )-}
{res}{txt}
{com}. gr_edit .legend.plotregion1.label[3].text.Arrpush Republicans
{res}{txt}
{com}. gr_edit .plotregion1.subtitle[1].text = {c -(}{c )-}
{res}{txt}
{com}. gr_edit .plotregion1.subtitle[1].text.Arrpush Effects on Authorship
{res}{txt}
{com}. gr_edit .plotregion1.subtitle[2].text = {c -(}{c )-}
{res}{txt}
{com}. gr_edit .plotregion1.subtitle[2].text.Arrpush Effects on Sponsorship
{res}{txt}
{com}. gr_edit .legend.Edit , style(cols(3)) style(rows(0)) keepstyles 
{res}{txt}
{com}. gr_edit .style.editstyle boxstyle(shadestyle(color(white))) editcopy
{res}{txt}
{com}. gr_edit .style.editstyle boxstyle(linestyle(color(white))) editcopy
{res}{txt}
{com}. gr_edit .plotregion1.subtitle[2].style.editstyle fillcolor(white) editcopy
{res}{txt}
{com}. gr_edit .plotregion1.subtitle[2].style.editstyle linestyle(color(black)) editcopy
{res}{txt}
{com}. gr_edit .plotregion1.plotregion1[2].style.editstyle boxstyle(linestyle(color(black))) editcopy
{res}{txt}
{com}. gr_edit .plotregion1.yaxis1[1].major.delete_tick 1
{res}{txt}
{com}. gr_edit .plotregion1.xaxis1[1].reset_rule -0.02 0.08 0.02 , tickset(major) ruletype(range) 
{res}{txt}
{com}. gr_edit .plotregion1.xaxis1[2].reset_rule -0.02 0.08 0.02 , tickset(major) ruletype(range) 
{res}{txt}
{com}. gr_edit .Edit , style(indiv_margin(medium))
{res}{txt}
{com}. 
. *Appendix Analyses
.  
. *Descriptive statistics (Table A-3)
. sum polauth if multauth==1

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}polauth {c |}{res}     10,834    .0773491    .2671569          0          1
{txt}
{com}. sum polspon oos_adopt_d judic chair leader female majmemb blackpct srty senate squire introlim sponlim termlim polcons smideol

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}polspon {c |}{res}     15,048    .1289208    .3351234          0          1
{txt}{space 1}oos_adopt_d {c |}{res}     15,048    .0760898    .2651506          0          1
{txt}{space 7}judic {c |}{res}     15,048    .1830808    .3867456          0          1
{txt}{space 7}chair {c |}{res}     15,048    .0124934     .111077          0          1
{txt}{space 6}leader {c |}{res}     15,048    .0745614     .262691          0          1
{txt}{hline 13}{c +}{hline 57}
{space 6}female {c |}{res}     15,048    .2143142    .4103594          0          1
{txt}{space 5}majmemb {c |}{res}     15,048     .609782    .4878153          0          1
{txt}{space 4}blackpct {c |}{res}     15,048    .1410998    .1965363          0        .99
{txt}{space 8}srty {c |}{res}     15,048    6.732656    6.917574          0         34
{txt}{space 6}senate {c |}{res}     15,048    .2528575    .4346644          0          1
{txt}{hline 13}{c +}{hline 57}
{space 6}squire {c |}{res}     15,048    .1672949    .0779734       .067       .459
{txt}{space 4}introlim {c |}{res}     15,048    .1521797    .3592069          0          1
{txt}{space 5}sponlim {c |}{res}     15,048    .1537081    .3606807          0          1
{txt}{space 5}termlim {c |}{res}     15,048    .2293993    .4204604          0          1
{txt}{space 5}polcons {c |}{res}     15,048    .4593277    .1545847       .138       .689
{txt}{hline 13}{c +}{hline 57}
{space 5}smideol {c |}{res}     15,048    .0066521    .7901558     -2.175      2.398
{txt}
{com}. 
. *Models with state fixed effects (Table A-5, Row 2) 
. logit polauth oos_adopt_d judic chair leader female majmemb blackpct srty senate i.statenum c.polcons##c.smideol if multauth==1, cl(polst)  

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-2949.5206}  
Iteration 1:{space 3}log pseudolikelihood = {res:-2599.6395}  
Iteration 2:{space 3}log pseudolikelihood = {res:-2407.9211}  
Iteration 3:{space 3}log pseudolikelihood = {res:-2404.1367}  
Iteration 4:{space 3}log pseudolikelihood = {res:-2404.1168}  
Iteration 5:{space 3}log pseudolikelihood = {res:-2404.1168}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}    10,834
{txt}{col 49}Wald chi2({res}25{txt}){col 67}= {res}    499.30
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-2404.1168{txt}{col 49}Pseudo R2{col 67}= {res}    0.1849

{txt}{ralign 85:(Std. Err. adjusted for {res:114} clusters in polst)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}            polauth{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}oos_adopt_d {c |}{col 21}{res}{space 2} .4477804{col 33}{space 2} .1862883{col 44}{space 1}    2.40{col 53}{space 3}0.016{col 61}{space 4}  .082662{col 74}{space 3} .8128988
{txt}{space 14}judic {c |}{col 21}{res}{space 2} .5717592{col 33}{space 2} .1596727{col 44}{space 1}    3.58{col 53}{space 3}0.000{col 61}{space 4} .2588066{col 74}{space 3} .8847119
{txt}{space 14}chair {c |}{col 21}{res}{space 2} .7354577{col 33}{space 2} .2589347{col 44}{space 1}    2.84{col 53}{space 3}0.005{col 61}{space 4} .2279551{col 74}{space 3}  1.24296
{txt}{space 13}leader {c |}{col 21}{res}{space 2} .1326276{col 33}{space 2} .1637336{col 44}{space 1}    0.81{col 53}{space 3}0.418{col 61}{space 4}-.1882844{col 74}{space 3} .4535396
{txt}{space 13}female {c |}{col 21}{res}{space 2} .1266437{col 33}{space 2} .1159069{col 44}{space 1}    1.09{col 53}{space 3}0.275{col 61}{space 4}-.1005296{col 74}{space 3} .3538171
{txt}{space 12}majmemb {c |}{col 21}{res}{space 2} .2512383{col 33}{space 2} .1614963{col 44}{space 1}    1.56{col 53}{space 3}0.120{col 61}{space 4}-.0652887{col 74}{space 3} .5677653
{txt}{space 11}blackpct {c |}{col 21}{res}{space 2}-.1547383{col 33}{space 2} .5247677{col 44}{space 1}   -0.29{col 53}{space 3}0.768{col 61}{space 4}-1.183264{col 74}{space 3} .8737876
{txt}{space 15}srty {c |}{col 21}{res}{space 2}-.0146932{col 33}{space 2} .0098342{col 44}{space 1}   -1.49{col 53}{space 3}0.135{col 61}{space 4}-.0339679{col 74}{space 3} .0045815
{txt}{space 13}senate {c |}{col 21}{res}{space 2}   .52701{col 33}{space 2} .2620581{col 44}{space 1}    2.01{col 53}{space 3}0.044{col 61}{space 4} .0133855{col 74}{space 3} 1.040634
{txt}{space 19} {c |}
{space 11}statenum {c |}
{space 16}AR  {c |}{col 21}{res}{space 2} 1.419844{col 33}{space 2} .5511449{col 44}{space 1}    2.58{col 53}{space 3}0.010{col 61}{space 4}   .33962{col 74}{space 3} 2.500068
{txt}{space 16}GA  {c |}{col 21}{res}{space 2} .8622435{col 33}{space 2}  .322535{col 44}{space 1}    2.67{col 53}{space 3}0.008{col 61}{space 4} .2300865{col 74}{space 3}   1.4944
{txt}{space 16}IL  {c |}{col 21}{res}{space 2} .4843442{col 33}{space 2} .3741095{col 44}{space 1}    1.29{col 53}{space 3}0.195{col 61}{space 4} -.248897{col 74}{space 3} 1.217585
{txt}{space 16}IN  {c |}{col 21}{res}{space 2} .4105879{col 33}{space 2} .4017087{col 44}{space 1}    1.02{col 53}{space 3}0.307{col 61}{space 4}-.3767467{col 74}{space 3} 1.197923
{txt}{space 16}KY  {c |}{col 21}{res}{space 2}-.0582646{col 33}{space 2} .3521597{col 44}{space 1}   -0.17{col 53}{space 3}0.869{col 61}{space 4} -.748485{col 74}{space 3} .6319558
{txt}{space 16}MN  {c |}{col 21}{res}{space 2} .7641052{col 33}{space 2} .3304137{col 44}{space 1}    2.31{col 53}{space 3}0.021{col 61}{space 4} .1165061{col 74}{space 3} 1.411704
{txt}{space 16}NC  {c |}{col 21}{res}{space 2}-.6360612{col 33}{space 2} .3358752{col 44}{space 1}   -1.89{col 53}{space 3}0.058{col 61}{space 4}-1.294364{col 74}{space 3} .0222421
{txt}{space 16}NE  {c |}{col 21}{res}{space 2} 1.197399{col 33}{space 2} .6304342{col 44}{space 1}    1.90{col 53}{space 3}0.058{col 61}{space 4}-.0382295{col 74}{space 3} 2.433027
{txt}{space 16}OH  {c |}{col 21}{res}{space 2} 2.073456{col 33}{space 2} .5171869{col 44}{space 1}    4.01{col 53}{space 3}0.000{col 61}{space 4} 1.059789{col 74}{space 3} 3.087124
{txt}{space 16}SC  {c |}{col 21}{res}{space 2} 2.474967{col 33}{space 2} .5447243{col 44}{space 1}    4.54{col 53}{space 3}0.000{col 61}{space 4} 1.407327{col 74}{space 3} 3.542607
{txt}{space 16}VT  {c |}{col 21}{res}{space 2}  .355088{col 33}{space 2} .4575001{col 44}{space 1}    0.78{col 53}{space 3}0.438{col 61}{space 4}-.5415956{col 74}{space 3} 1.251772
{txt}{space 16}WA  {c |}{col 21}{res}{space 2} 1.808662{col 33}{space 2} .3816342{col 44}{space 1}    4.74{col 53}{space 3}0.000{col 61}{space 4} 1.060673{col 74}{space 3} 2.556651
{txt}{space 16}WI  {c |}{col 21}{res}{space 2} 2.476587{col 33}{space 2} .3856548{col 44}{space 1}    6.42{col 53}{space 3}0.000{col 61}{space 4} 1.720718{col 74}{space 3} 3.232457
{txt}{space 19} {c |}
{space 12}polcons {c |}{col 21}{res}{space 2} 2.645163{col 33}{space 2} .9652133{col 44}{space 1}    2.74{col 53}{space 3}0.006{col 61}{space 4} .7533798{col 74}{space 3} 4.536947
{txt}{space 12}smideol {c |}{col 21}{res}{space 2}-2.204201{col 33}{space 2} .4139964{col 44}{space 1}   -5.32{col 53}{space 3}0.000{col 61}{space 4}-3.015619{col 74}{space 3}-1.392783
{txt}{space 19} {c |}
c.polcons#c.smideol {c |}{col 21}{res}{space 2} 4.865757{col 33}{space 2} .7840721{col 44}{space 1}    6.21{col 53}{space 3}0.000{col 61}{space 4} 3.329004{col 74}{space 3}  6.40251
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2}-5.622146{col 33}{space 2} .5727368{col 44}{space 1}   -9.82{col 53}{space 3}0.000{col 61}{space 4}-6.744689{col 74}{space 3}-4.499602
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit polspon oos_adopt_d judic chair leader female majmemb blackpct srty senate i.statenum c.polcons##c.smideol, cl(polst)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-5783.3979}  
Iteration 1:{space 3}log pseudolikelihood = {res:-5074.7305}  
Iteration 2:{space 3}log pseudolikelihood = {res:-4958.0118}  
Iteration 3:{space 3}log pseudolikelihood = {res:-4956.0852}  
Iteration 4:{space 3}log pseudolikelihood = {res:-4956.0748}  
Iteration 5:{space 3}log pseudolikelihood = {res:-4956.0748}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}    15,048
{txt}{col 49}Wald chi2({res}31{txt}){col 67}= {res}    433.87
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-4956.0748{txt}{col 49}Pseudo R2{col 67}= {res}    0.1431

{txt}{ralign 85:(Std. Err. adjusted for {res:157} clusters in polst)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}            polspon{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}oos_adopt_d {c |}{col 21}{res}{space 2} .4165618{col 33}{space 2} .1195882{col 44}{space 1}    3.48{col 53}{space 3}0.000{col 61}{space 4} .1821733{col 74}{space 3} .6509503
{txt}{space 14}judic {c |}{col 21}{res}{space 2} .4296191{col 33}{space 2} .0991812{col 44}{space 1}    4.33{col 53}{space 3}0.000{col 61}{space 4} .2352276{col 74}{space 3} .6240106
{txt}{space 14}chair {c |}{col 21}{res}{space 2} .5359768{col 33}{space 2} .1928399{col 44}{space 1}    2.78{col 53}{space 3}0.005{col 61}{space 4} .1580175{col 74}{space 3} .9139361
{txt}{space 13}leader {c |}{col 21}{res}{space 2} .1654394{col 33}{space 2} .0979112{col 44}{space 1}    1.69{col 53}{space 3}0.091{col 61}{space 4}-.0264631{col 74}{space 3} .3573419
{txt}{space 13}female {c |}{col 21}{res}{space 2} .0336937{col 33}{space 2} .0844305{col 44}{space 1}    0.40{col 53}{space 3}0.690{col 61}{space 4}-.1317871{col 74}{space 3} .1991744
{txt}{space 12}majmemb {c |}{col 21}{res}{space 2} .2269218{col 33}{space 2} .1252266{col 44}{space 1}    1.81{col 53}{space 3}0.070{col 61}{space 4}-.0185179{col 74}{space 3} .4723615
{txt}{space 11}blackpct {c |}{col 21}{res}{space 2}-.2647386{col 33}{space 2}  .297485{col 44}{space 1}   -0.89{col 53}{space 3}0.374{col 61}{space 4}-.8477984{col 74}{space 3} .3183212
{txt}{space 15}srty {c |}{col 21}{res}{space 2}-.0294345{col 33}{space 2} .0071157{col 44}{space 1}   -4.14{col 53}{space 3}0.000{col 61}{space 4}-.0433809{col 74}{space 3}-.0154881
{txt}{space 13}senate {c |}{col 21}{res}{space 2} .7501812{col 33}{space 2} .2006184{col 44}{space 1}    3.74{col 53}{space 3}0.000{col 61}{space 4} .3569762{col 74}{space 3} 1.143386
{txt}{space 19} {c |}
{space 11}statenum {c |}
{space 16}AR  {c |}{col 21}{res}{space 2} 2.305697{col 33}{space 2} .4473172{col 44}{space 1}    5.15{col 53}{space 3}0.000{col 61}{space 4} 1.428972{col 74}{space 3} 3.182423
{txt}{space 16}CT  {c |}{col 21}{res}{space 2} 2.313249{col 33}{space 2} .4147763{col 44}{space 1}    5.58{col 53}{space 3}0.000{col 61}{space 4} 1.500303{col 74}{space 3} 3.126196
{txt}{space 16}GA  {c |}{col 21}{res}{space 2} .7755148{col 33}{space 2}  .313118{col 44}{space 1}    2.48{col 53}{space 3}0.013{col 61}{space 4} .1618149{col 74}{space 3} 1.389215
{txt}{space 16}IL  {c |}{col 21}{res}{space 2} 1.669722{col 33}{space 2} .4299672{col 44}{space 1}    3.88{col 53}{space 3}0.000{col 61}{space 4} .8270017{col 74}{space 3} 2.512442
{txt}{space 16}IN  {c |}{col 21}{res}{space 2} 1.389333{col 33}{space 2} .3472538{col 44}{space 1}    4.00{col 53}{space 3}0.000{col 61}{space 4} .7087278{col 74}{space 3} 2.069938
{txt}{space 16}KY  {c |}{col 21}{res}{space 2} 2.596239{col 33}{space 2} .4776984{col 44}{space 1}    5.43{col 53}{space 3}0.000{col 61}{space 4} 1.659967{col 74}{space 3} 3.532511
{txt}{space 16}MN  {c |}{col 21}{res}{space 2} .6410764{col 33}{space 2} .3390824{col 44}{space 1}    1.89{col 53}{space 3}0.059{col 61}{space 4} -.023513{col 74}{space 3} 1.305666
{txt}{space 16}MO  {c |}{col 21}{res}{space 2} 1.535497{col 33}{space 2} .6105506{col 44}{space 1}    2.51{col 53}{space 3}0.012{col 61}{space 4} .3388397{col 74}{space 3} 2.732154
{txt}{space 16}MS  {c |}{col 21}{res}{space 2} .7443131{col 33}{space 2} .4426686{col 44}{space 1}    1.68{col 53}{space 3}0.093{col 61}{space 4}-.1233014{col 74}{space 3} 1.611928
{txt}{space 16}MT  {c |}{col 21}{res}{space 2} 1.225596{col 33}{space 2} .4811691{col 44}{space 1}    2.55{col 53}{space 3}0.011{col 61}{space 4} .2825219{col 74}{space 3}  2.16867
{txt}{space 16}NC  {c |}{col 21}{res}{space 2}  2.49568{col 33}{space 2} .5116845{col 44}{space 1}    4.88{col 53}{space 3}0.000{col 61}{space 4} 1.492797{col 74}{space 3} 3.498563
{txt}{space 16}NE  {c |}{col 21}{res}{space 2} 1.205139{col 33}{space 2} .7019858{col 44}{space 1}    1.72{col 53}{space 3}0.086{col 61}{space 4}-.1707275{col 74}{space 3} 2.581006
{txt}{space 16}OH  {c |}{col 21}{res}{space 2} 2.941793{col 33}{space 2} .4589807{col 44}{space 1}    6.41{col 53}{space 3}0.000{col 61}{space 4} 2.042208{col 74}{space 3} 3.841379
{txt}{space 16}OK  {c |}{col 21}{res}{space 2} .6121341{col 33}{space 2} .3838546{col 44}{space 1}    1.59{col 53}{space 3}0.111{col 61}{space 4}-.1402072{col 74}{space 3} 1.364475
{txt}{space 16}SC  {c |}{col 21}{res}{space 2} 2.470492{col 33}{space 2} .5576309{col 44}{space 1}    4.43{col 53}{space 3}0.000{col 61}{space 4} 1.377555{col 74}{space 3} 3.563428
{txt}{space 16}VA  {c |}{col 21}{res}{space 2}  2.16638{col 33}{space 2} .3956878{col 44}{space 1}    5.47{col 53}{space 3}0.000{col 61}{space 4} 1.390847{col 74}{space 3} 2.941914
{txt}{space 16}VT  {c |}{col 21}{res}{space 2} .9380139{col 33}{space 2} .6886792{col 44}{space 1}    1.36{col 53}{space 3}0.173{col 61}{space 4}-.4117725{col 74}{space 3}   2.2878
{txt}{space 16}WA  {c |}{col 21}{res}{space 2} 1.700024{col 33}{space 2}  .379788{col 44}{space 1}    4.48{col 53}{space 3}0.000{col 61}{space 4}  .955653{col 74}{space 3} 2.444395
{txt}{space 16}WI  {c |}{col 21}{res}{space 2} 2.436283{col 33}{space 2} .3898863{col 44}{space 1}    6.25{col 53}{space 3}0.000{col 61}{space 4}  1.67212{col 74}{space 3} 3.200446
{txt}{space 19} {c |}
{space 12}polcons {c |}{col 21}{res}{space 2} 2.853605{col 33}{space 2} .7060317{col 44}{space 1}    4.04{col 53}{space 3}0.000{col 61}{space 4} 1.469808{col 74}{space 3} 4.237401
{txt}{space 12}smideol {c |}{col 21}{res}{space 2}-2.388583{col 33}{space 2} .3331842{col 44}{space 1}   -7.17{col 53}{space 3}0.000{col 61}{space 4}-3.041612{col 74}{space 3}-1.735554
{txt}{space 19} {c |}
c.polcons#c.smideol {c |}{col 21}{res}{space 2} 5.165524{col 33}{space 2} .6325865{col 44}{space 1}    8.17{col 53}{space 3}0.000{col 61}{space 4} 3.925678{col 74}{space 3} 6.405371
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2} -5.57824{col 33}{space 2} .4589413{col 44}{space 1}  -12.15{col 53}{space 3}0.000{col 61}{space 4}-6.477748{col 74}{space 3}-4.678732
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Coarsened exact matching (Table A-5, Row 3)
. cem polst(#0) chair judic majmemb blackpct smideol if match_polst==1 , treatment(oos_adopt_d)
{res}
{txt}Matching Summary:
-----------------
Number of strata: {res}3478
{txt}Number of matched strata: {res}498

           {txt}   0     1
      All  {res}7997  1109
{txt}  Matched  {res}2612   823
{txt}Unmatched  {res}5385   286


{txt}Multivariate L1 distance: {res}.47228966

{txt}Univariate imbalance:

               L1     mean      min      25%      50%      75%      max
   polst  {res}2.6e-15  1.5e-11        0        0        0        0        0
{txt}   chair  {res}1.1e-16  6.1e-18        0        0        0        0        0
{txt}   judic  {res}8.0e-16  5.0e-16        0        0        0        0        0
{txt} majmemb  {res}1.3e-15  1.0e-15        0        0        0        0        0
{txt}blackpct  {res} .14747  -.00834        0  -.00595     -.02        0     -.04
{txt} smideol  {res} .08345   .01653     .095     .014      .04      .02     .006
{txt}
{com}. logit polauth oos_adopt_d if multauth==1 & match_polst==1  [iw=cem_weights] , cl(polst)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-754.08226}  
Iteration 1:{space 3}log pseudolikelihood = {res:-751.77898}  
Iteration 2:{space 3}log pseudolikelihood = {res:-751.75402}  
Iteration 3:{space 3}log pseudolikelihood = {res:-751.75401}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     2,762
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}      4.92
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0265
{txt}Log pseudolikelihood = {res}-751.75401{txt}{col 49}Pseudo R2{col 67}= {res}    0.0031

{txt}{ralign 78:(Std. Err. adjusted for {res:68} clusters in polst)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     polauth{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}oos_adopt_d {c |}{col 14}{res}{space 2} .3446775{col 26}{space 2} .1553678{col 37}{space 1}    2.22{col 46}{space 3}0.027{col 54}{space 4} .0401622{col 67}{space 3} .6491928
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-2.610663{col 26}{space 2} .3846092{col 37}{space 1}   -6.79{col 46}{space 3}0.000{col 54}{space 4}-3.364484{col 67}{space 3}-1.856843
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. logit polspon oos_adopt_d if match_polst==1 [iw=cem_weights] , cl(polst)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1417.6821}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1412.0631}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1412.0174}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1412.0174}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     3,435
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}     14.88
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0001
{txt}Log pseudolikelihood = {res}-1412.0174{txt}{col 49}Pseudo R2{col 67}= {res}    0.0040

{txt}{ralign 78:(Std. Err. adjusted for {res:91} clusters in polst)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}     polspon{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}oos_adopt_d {c |}{col 14}{res}{space 2} .3676651{col 26}{space 2} .0953238{col 37}{space 1}    3.86{col 46}{space 3}0.000{col 54}{space 4} .1808338{col 67}{space 3} .5544963
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-1.876949{col 26}{space 2} .2487567{col 37}{space 1}   -7.55{col 46}{space 3}0.000{col 54}{space 4}-2.364503{col 67}{space 3}-1.389395
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Excluding the bills that weren’t passed (Table A-5, Row 6)
. *Authorship model
. quietly  melogit polauth oos_adopt_d judic chair leader female majmemb blackpct srty senate squire introlim termlim c.polcons##c.smideol if multauth==1 & introonly==0 || _all: R.polnum
{txt}
{com}. matrix e1 = e(b)
{txt}
{com}. melogit polauth oos_adopt_d judic chair leader female majmemb blackpct srty senate squire introlim termlim c.polcons##c.smideol if multauth==1 & introonly==0 || _all: R.polnum|| statenum:, from(e1) difficult
{res}{txt}{p 0 0 2}
note: crossed random-effects model specified; option intmethod(laplace) implied
{p_end}

Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-2329.2579}  
Iteration 1:{space 3}log likelihood = {res:-2149.5976}  
Iteration 2:{space 3}log likelihood = {res:-2144.1405}  
Iteration 3:{space 3}log likelihood = {res:-2144.0944}  
Iteration 4:{space 3}log likelihood = {res:-2144.0944}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-1979.8837}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-1979.8837}  
Iteration 1:{space 3}log likelihood = {res:-1971.8574}  (not concave)
Iteration 2:{space 3}log likelihood = {res:-1971.7445}  (not concave)
Iteration 3:{space 3}log likelihood = {res:-1971.2053}  
Iteration 4:{space 3}log likelihood = {res:-1962.8339}  (not concave)
Iteration 5:{space 3}log likelihood = {res: -1962.814}  (not concave)
Iteration 6:{space 3}log likelihood = {res:-1962.7053}  (not concave)
Iteration 7:{space 3}log likelihood = {res:-1962.7015}  
Iteration 8:{space 3}log likelihood = {res: -1962.698}  
Iteration 9:{space 3}log likelihood = {res: -1962.698}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}     8,601

{txt}{hline 16}{c TT}{hline 44}
{col 17}{txt}{c |}{col 23}No. of{col 36}Observations per Group
{col 2}{txt}Group Variable{col 17}{c |}{col 23}Groups{col 33}Minimum{col 44}Average{col 55}Maximum
{txt}{hline 16}{c +}{hline 44}
{col 12}{res}_all{col 17}{txt}{c |}{res}{col 21}       1{col 31}    8,601{col 42}  8,601.0{col 53}    8,601
{col 8}{res}statenum{col 17}{txt}{c |}{res}{col 21}      14{col 31}      245{col 42}    614.4{col 53}      970
{txt}{hline 16}{c BT}{hline 44}

{txt}Integration method: {col 25}{res}laplace

{col 49}{txt}Wald chi2({res}15{txt}){col 67}={res}{col 70}   214.92
{txt}Log likelihood = {res} -1962.698{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}            polauth{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}oos_adopt_d {c |}{col 21}{res}{space 2} .3522229{col 33}{space 2}  .142883{col 44}{space 1}    2.47{col 53}{space 3}0.014{col 61}{space 4} .0721772{col 74}{space 3} .6322685
{txt}{space 14}judic {c |}{col 21}{res}{space 2} .5411392{col 33}{space 2}  .105695{col 44}{space 1}    5.12{col 53}{space 3}0.000{col 61}{space 4} .3339808{col 74}{space 3} .7482975
{txt}{space 14}chair {c |}{col 21}{res}{space 2} .5779978{col 33}{space 2} .2958568{col 44}{space 1}    1.95{col 53}{space 3}0.051{col 61}{space 4}-.0018709{col 74}{space 3} 1.157866
{txt}{space 13}leader {c |}{col 21}{res}{space 2} .1322403{col 33}{space 2}  .165153{col 44}{space 1}    0.80{col 53}{space 3}0.423{col 61}{space 4}-.1914537{col 74}{space 3} .4559342
{txt}{space 13}female {c |}{col 21}{res}{space 2} .2115865{col 33}{space 2} .1049918{col 44}{space 1}    2.02{col 53}{space 3}0.044{col 61}{space 4} .0058063{col 74}{space 3} .4173667
{txt}{space 12}majmemb {c |}{col 21}{res}{space 2}  .351586{col 33}{space 2} .0986482{col 44}{space 1}    3.56{col 53}{space 3}0.000{col 61}{space 4} .1582391{col 74}{space 3} .5449328
{txt}{space 11}blackpct {c |}{col 21}{res}{space 2}-.8289768{col 33}{space 2} .3212977{col 44}{space 1}   -2.58{col 53}{space 3}0.010{col 61}{space 4}-1.458709{col 74}{space 3}-.1992449
{txt}{space 15}srty {c |}{col 21}{res}{space 2}-.0180101{col 33}{space 2} .0074374{col 44}{space 1}   -2.42{col 53}{space 3}0.015{col 61}{space 4}-.0325872{col 74}{space 3}-.0034331
{txt}{space 13}senate {c |}{col 21}{res}{space 2} .5839305{col 33}{space 2} .1123456{col 44}{space 1}    5.20{col 53}{space 3}0.000{col 61}{space 4} .3637371{col 74}{space 3} .8041239
{txt}{space 13}squire {c |}{col 21}{res}{space 2} 5.946156{col 33}{space 2} 2.210807{col 44}{space 1}    2.69{col 53}{space 3}0.007{col 61}{space 4} 1.613054{col 74}{space 3} 10.27926
{txt}{space 11}introlim {c |}{col 21}{res}{space 2}-1.004662{col 33}{space 2} .6548915{col 44}{space 1}   -1.53{col 53}{space 3}0.125{col 61}{space 4}-2.288226{col 74}{space 3} .2789013
{txt}{space 12}termlim {c |}{col 21}{res}{space 2} .1788816{col 33}{space 2} .2549322{col 44}{space 1}    0.70{col 53}{space 3}0.483{col 61}{space 4}-.3207763{col 74}{space 3} .6785395
{txt}{space 12}polcons {c |}{col 21}{res}{space 2} 1.673611{col 33}{space 2} 1.019446{col 44}{space 1}    1.64{col 53}{space 3}0.101{col 61}{space 4} -.324467{col 74}{space 3} 3.671689
{txt}{space 12}smideol {c |}{col 21}{res}{space 2}-2.026949{col 33}{space 2}  .248134{col 44}{space 1}   -8.17{col 53}{space 3}0.000{col 61}{space 4}-2.513283{col 74}{space 3}-1.540615
{txt}{space 19} {c |}
c.polcons#c.smideol {c |}{col 21}{res}{space 2} 4.415517{col 33}{space 2} .4914273{col 44}{space 1}    8.99{col 53}{space 3}0.000{col 61}{space 4} 3.452337{col 74}{space 3} 5.378696
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2}-5.165574{col 33}{space 2} .6959428{col 44}{space 1}   -7.42{col 53}{space 3}0.000{col 61}{space 4}-6.529597{col 74}{space 3}-3.801551
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}_all>polnum        {col 21}{txt}{c |}
{space 10}var(_cons){c |}{col 21}{res}{space 2} .1978409{col 33}{space 2} .0969004{col 61}{space 4} .0757541{col 74}{space 3} .5166849
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}statenum           {col 21}{txt}{c |}
{space 10}var(_cons){c |}{col 21}{res}{space 2} .6388456{col 33}{space 2} .2665268{col 61}{space 4} .2820162{col 74}{space 3} 1.447164
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}chi2({res}2{txt}) ={res} 362.79{col 59}{txt}Prob > chi2 ={res}{col 73}0.0000

{txt}{p 0 6 4 79}Note: {help j_mixedlr##|_new:LR test is conservative} and provided only for reference.{p_end}

{com}. *Sponsorship model
. quietly melogit polspon oos_adopt_d judic chair leader female majmemb blackpct srty senate squire sponlim termlim c.polcons##c.smideol if introonly==0 || _all: R.polnum
{txt}
{com}. matrix f1 = e(b)
{txt}
{com}. melogit polspon oos_adopt_d judic chair leader female majmemb blackpct srty senate squire sponlim termlim c.polcons##c.smideol if introonly==0  || _all: R.polnum|| statenum:, from(f1) difficult
{res}{txt}{p 0 0 2}
note: crossed random-effects model specified; option intmethod(laplace) implied
{p_end}

Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-4625.8051}  
Iteration 1:{space 3}log likelihood = {res:-4514.1334}  
Iteration 2:{space 3}log likelihood = {res:-4512.0802}  
Iteration 3:{space 3}log likelihood = {res:-4512.0798}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-4306.8585}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-4306.8585}  (not concave)
Iteration 1:{space 3}log likelihood = {res:-4262.0825}  (not concave)
Iteration 2:{space 3}log likelihood = {res:-4261.5212}  
Iteration 3:{space 3}log likelihood = {res:-4261.3429}  
Iteration 4:{space 3}log likelihood = {res:-4261.3352}  
Iteration 5:{space 3}log likelihood = {res:-4261.3324}  
Iteration 6:{space 3}log likelihood = {res:-4261.3302}  
Iteration 7:{space 3}log likelihood = {res:-4261.3296}  
Iteration 8:{space 3}log likelihood = {res:-4261.3296}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    12,105

{txt}{hline 16}{c TT}{hline 44}
{col 17}{txt}{c |}{col 23}No. of{col 36}Observations per Group
{col 2}{txt}Group Variable{col 17}{c |}{col 23}Groups{col 33}Minimum{col 44}Average{col 55}Maximum
{txt}{hline 16}{c +}{hline 44}
{col 12}{res}_all{col 17}{txt}{c |}{res}{col 21}       1{col 31}   12,105{col 42} 12,105.0{col 53}   12,105
{col 8}{res}statenum{col 17}{txt}{c |}{res}{col 21}      20{col 31}      245{col 42}    605.3{col 53}      970
{txt}{hline 16}{c BT}{hline 44}

{txt}Integration method: {col 25}{res}laplace

{col 49}{txt}Wald chi2({res}15{txt}){col 67}={res}{col 70}   483.39
{txt}Log likelihood = {res}-4261.3296{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}            polspon{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}oos_adopt_d {c |}{col 21}{res}{space 2} .3045586{col 33}{space 2} .0958332{col 44}{space 1}    3.18{col 53}{space 3}0.001{col 61}{space 4}  .116729{col 74}{space 3} .4923882
{txt}{space 14}judic {c |}{col 21}{res}{space 2} .4267987{col 33}{space 2} .0690341{col 44}{space 1}    6.18{col 53}{space 3}0.000{col 61}{space 4} .2914943{col 74}{space 3} .5621031
{txt}{space 14}chair {c |}{col 21}{res}{space 2} .4403124{col 33}{space 2} .2145039{col 44}{space 1}    2.05{col 53}{space 3}0.040{col 61}{space 4} .0198925{col 74}{space 3} .8607323
{txt}{space 13}leader {c |}{col 21}{res}{space 2}  .183247{col 33}{space 2}   .10881{col 44}{space 1}    1.68{col 53}{space 3}0.092{col 61}{space 4}-.0300167{col 74}{space 3} .3965108
{txt}{space 13}female {c |}{col 21}{res}{space 2} .0773682{col 33}{space 2} .0711767{col 44}{space 1}    1.09{col 53}{space 3}0.277{col 61}{space 4}-.0621356{col 74}{space 3}  .216872
{txt}{space 12}majmemb {c |}{col 21}{res}{space 2} .2543613{col 33}{space 2} .0608054{col 44}{space 1}    4.18{col 53}{space 3}0.000{col 61}{space 4} .1351848{col 74}{space 3} .3735377
{txt}{space 11}blackpct {c |}{col 21}{res}{space 2}-.7389426{col 33}{space 2} .2027774{col 44}{space 1}   -3.64{col 53}{space 3}0.000{col 61}{space 4}-1.136379{col 74}{space 3}-.3415063
{txt}{space 15}srty {c |}{col 21}{res}{space 2}-.0333749{col 33}{space 2} .0048956{col 44}{space 1}   -6.82{col 53}{space 3}0.000{col 61}{space 4}-.0429701{col 74}{space 3}-.0237798
{txt}{space 13}senate {c |}{col 21}{res}{space 2} .8059579{col 33}{space 2}  .075827{col 44}{space 1}   10.63{col 53}{space 3}0.000{col 61}{space 4} .6573397{col 74}{space 3} .9545761
{txt}{space 13}squire {c |}{col 21}{res}{space 2}  6.80554{col 33}{space 2} 1.767732{col 44}{space 1}    3.85{col 53}{space 3}0.000{col 61}{space 4} 3.340848{col 74}{space 3} 10.27023
{txt}{space 12}sponlim {c |}{col 21}{res}{space 2} .2152015{col 33}{space 2}  .232355{col 44}{space 1}    0.93{col 53}{space 3}0.354{col 61}{space 4}-.2402059{col 74}{space 3} .6706089
{txt}{space 12}termlim {c |}{col 21}{res}{space 2} .4933407{col 33}{space 2} .2296068{col 44}{space 1}    2.15{col 53}{space 3}0.032{col 61}{space 4} .0433197{col 74}{space 3} .9433617
{txt}{space 12}polcons {c |}{col 21}{res}{space 2} 1.980268{col 33}{space 2} .9588967{col 44}{space 1}    2.07{col 53}{space 3}0.039{col 61}{space 4}  .100865{col 74}{space 3} 3.859671
{txt}{space 12}smideol {c |}{col 21}{res}{space 2}-1.994102{col 33}{space 2}  .155713{col 44}{space 1}  -12.81{col 53}{space 3}0.000{col 61}{space 4}-2.299294{col 74}{space 3} -1.68891
{txt}{space 19} {c |}
c.polcons#c.smideol {c |}{col 21}{res}{space 2} 4.290714{col 33}{space 2} .3041968{col 44}{space 1}   14.11{col 53}{space 3}0.000{col 61}{space 4} 3.694499{col 74}{space 3} 4.886929
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2}-4.704704{col 33}{space 2} .5996716{col 44}{space 1}   -7.85{col 53}{space 3}0.000{col 61}{space 4}-5.880039{col 74}{space 3}-3.529369
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}_all>polnum        {col 21}{txt}{c |}
{space 10}var(_cons){c |}{col 21}{res}{space 2} .2823604{col 33}{space 2} .1251186{col 61}{space 4} .1184737{col 74}{space 3} .6729542
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}statenum           {col 21}{txt}{c |}
{space 10}var(_cons){c |}{col 21}{res}{space 2} .6895633{col 33}{space 2} .2514597{col 61}{space 4} .3374199{col 74}{space 3} 1.409216
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}chi2({res}2{txt}) ={res} 501.50{col 59}{txt}Prob > chi2 ={res}{col 73}0.0000

{txt}{p 0 6 4 79}Note: {help j_mixedlr##|_new:LR test is conservative} and provided only for reference.{p_end}

{com}. 
. *Robustness check in defining oos media markets (Table A-5, Row 7)
. *Authorship model
. quietly melogit polauth oos_adopt25_d judic chair leader female majmemb blackpct srty senate squire introlim termlim c.polcons##c.smideol if multauth==1 || _all: R.polnum
{txt}
{com}. matrix i1 = e(b)
{txt}
{com}. melogit polauth oos_adopt25_d judic chair leader female majmemb blackpct srty senate squire introlim termlim c.polcons##c.smideol if multauth==1 || _all: R.polnum|| statenum:, from(i1) difficult
{res}{txt}{p 0 0 2}
note: crossed random-effects model specified; option intmethod(laplace) implied
{p_end}

Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-2821.4489}  
Iteration 1:{space 3}log likelihood = {res:-2564.6622}  
Iteration 2:{space 3}log likelihood = {res: -2554.434}  
Iteration 3:{space 3}log likelihood = {res:-2554.3528}  
Iteration 4:{space 3}log likelihood = {res:-2554.3527}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-2400.5509}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-2400.5509}  (not concave)
Iteration 1:{space 3}log likelihood = {res:-2396.2128}  (not concave)
Iteration 2:{space 3}log likelihood = {res: -2389.643}  (not concave)
Iteration 3:{space 3}log likelihood = {res:-2388.3839}  (not concave)
Iteration 4:{space 3}log likelihood = {res:-2386.0012}  
Iteration 5:{space 3}log likelihood = {res:-2385.7544}  
Iteration 6:{space 3}log likelihood = {res:-2385.7446}  
Iteration 7:{space 3}log likelihood = {res:-2385.7439}  
Iteration 8:{space 3}log likelihood = {res:-2385.7439}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    10,834

{txt}{hline 16}{c TT}{hline 44}
{col 17}{txt}{c |}{col 23}No. of{col 36}Observations per Group
{col 2}{txt}Group Variable{col 17}{c |}{col 23}Groups{col 33}Minimum{col 44}Average{col 55}Maximum
{txt}{hline 16}{c +}{hline 44}
{col 12}{res}_all{col 17}{txt}{c |}{res}{col 21}       1{col 31}   10,834{col 42} 10,834.0{col 53}   10,834
{col 8}{res}statenum{col 17}{txt}{c |}{res}{col 21}      14{col 31}      343{col 42}    773.9{col 53}    1,273
{txt}{hline 16}{c BT}{hline 44}

{txt}Integration method: {col 25}{res}laplace

{col 49}{txt}Wald chi2({res}15{txt}){col 67}={res}{col 70}   308.84
{txt}Log likelihood = {res}-2385.7439{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}            polauth{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}oos_adopt25_d {c |}{col 21}{res}{space 2} .2483752{col 33}{space 2} .1482029{col 44}{space 1}    1.68{col 53}{space 3}0.094{col 61}{space 4}-.0420971{col 74}{space 3} .5388476
{txt}{space 14}judic {c |}{col 21}{res}{space 2}  .576536{col 33}{space 2} .0957123{col 44}{space 1}    6.02{col 53}{space 3}0.000{col 61}{space 4} .3889434{col 74}{space 3} .7641287
{txt}{space 14}chair {c |}{col 21}{res}{space 2} .7542042{col 33}{space 2}  .258643{col 44}{space 1}    2.92{col 53}{space 3}0.004{col 61}{space 4} .2472733{col 74}{space 3} 1.261135
{txt}{space 13}leader {c |}{col 21}{res}{space 2} .1273863{col 33}{space 2} .1506973{col 44}{space 1}    0.85{col 53}{space 3}0.398{col 61}{space 4} -.167975{col 74}{space 3} .4227477
{txt}{space 13}female {c |}{col 21}{res}{space 2} .1204799{col 33}{space 2} .0960399{col 44}{space 1}    1.25{col 53}{space 3}0.210{col 61}{space 4}-.0677549{col 74}{space 3} .3087146
{txt}{space 12}majmemb {c |}{col 21}{res}{space 2} .2546787{col 33}{space 2} .0886853{col 44}{space 1}    2.87{col 53}{space 3}0.004{col 61}{space 4} .0808588{col 74}{space 3} .4284986
{txt}{space 11}blackpct {c |}{col 21}{res}{space 2}-.1567698{col 33}{space 2} .2774005{col 44}{space 1}   -0.57{col 53}{space 3}0.572{col 61}{space 4}-.7004647{col 74}{space 3} .3869251
{txt}{space 15}srty {c |}{col 21}{res}{space 2}-.0151162{col 33}{space 2} .0066712{col 44}{space 1}   -2.27{col 53}{space 3}0.023{col 61}{space 4}-.0281915{col 74}{space 3}-.0020408
{txt}{space 13}senate {c |}{col 21}{res}{space 2} .5921251{col 33}{space 2} .1013781{col 44}{space 1}    5.84{col 53}{space 3}0.000{col 61}{space 4} .3934276{col 74}{space 3} .7908225
{txt}{space 13}squire {c |}{col 21}{res}{space 2} 4.122382{col 33}{space 2} 1.904915{col 44}{space 1}    2.16{col 53}{space 3}0.030{col 61}{space 4} .3888179{col 74}{space 3} 7.855947
{txt}{space 11}introlim {c |}{col 21}{res}{space 2}-.9658581{col 33}{space 2} .5815057{col 44}{space 1}   -1.66{col 53}{space 3}0.097{col 61}{space 4}-2.105588{col 74}{space 3} .1738722
{txt}{space 12}termlim {c |}{col 21}{res}{space 2} .3413033{col 33}{space 2}  .234426{col 44}{space 1}    1.46{col 53}{space 3}0.145{col 61}{space 4}-.1181633{col 74}{space 3} .8007698
{txt}{space 12}polcons {c |}{col 21}{res}{space 2} 1.983369{col 33}{space 2} .9381258{col 44}{space 1}    2.11{col 53}{space 3}0.034{col 61}{space 4} .1446767{col 74}{space 3} 3.822062
{txt}{space 12}smideol {c |}{col 21}{res}{space 2}-2.279364{col 33}{space 2} .1991746{col 44}{space 1}  -11.44{col 53}{space 3}0.000{col 61}{space 4}-2.669739{col 74}{space 3}-1.888989
{txt}{space 19} {c |}
c.polcons#c.smideol {c |}{col 21}{res}{space 2} 5.022403{col 33}{space 2} .3869974{col 44}{space 1}   12.98{col 53}{space 3}0.000{col 61}{space 4} 4.263902{col 74}{space 3} 5.780904
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2}-5.122754{col 33}{space 2} .6246199{col 44}{space 1}   -8.20{col 53}{space 3}0.000{col 61}{space 4}-6.346986{col 74}{space 3}-3.898521
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}_all>polnum        {col 21}{txt}{c |}
{space 10}var(_cons){c |}{col 21}{res}{space 2} .2377318{col 33}{space 2} .1128561{col 61}{space 4} .0937572{col 74}{space 3} .6027951
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}statenum           {col 21}{txt}{c |}
{space 10}var(_cons){c |}{col 21}{res}{space 2} .5034869{col 33}{space 2} .2050432{col 61}{space 4} .2266415{col 74}{space 3} 1.118502
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}chi2({res}2{txt}) ={res} 337.22{col 59}{txt}Prob > chi2 ={res}{col 73}0.0000

{txt}{p 0 6 4 79}Note: {help j_mixedlr##|_new:LR test is conservative} and provided only for reference.{p_end}

{com}. *Sponsorship model
. quietly melogit polspon oos_adopt25_d judic chair leader female majmemb blackpct srty senate squire sponlim termlim c.polcons##c.smideol || _all: R.polnum
{txt}
{com}. matrix j1 = e(b)
{txt}
{com}. melogit polspon oos_adopt25_d judic chair leader female majmemb blackpct srty senate squire sponlim termlim c.polcons##c.smideol || _all: R.polnum|| statenum:, from(j1) difficult
{res}{txt}{p 0 0 2}
note: crossed random-effects model specified; option intmethod(laplace) implied
{p_end}

Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-5374.8781}  
Iteration 1:{space 3}log likelihood = {res:-5194.7174}  
Iteration 2:{space 3}log likelihood = {res:-5191.9643}  
Iteration 3:{space 3}log likelihood = {res:-5191.9625}  
Iteration 4:{space 3}log likelihood = {res:-5191.9625}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-4949.9726}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-4949.9726}  (not concave)
Iteration 1:{space 3}log likelihood = {res:-4914.3872}  (not concave)
Iteration 2:{space 3}log likelihood = {res:-4914.0113}  
Iteration 3:{space 3}log likelihood = {res:-4913.8856}  
Iteration 4:{space 3}log likelihood = {res:-4913.8678}  
Iteration 5:{space 3}log likelihood = {res:-4913.8581}  
Iteration 6:{space 3}log likelihood = {res:-4913.8539}  
Iteration 7:{space 3}log likelihood = {res:-4913.8505}  
Iteration 8:{space 3}log likelihood = {res:-4913.8504}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    15,048

{txt}{hline 16}{c TT}{hline 44}
{col 17}{txt}{c |}{col 23}No. of{col 36}Observations per Group
{col 2}{txt}Group Variable{col 17}{c |}{col 23}Groups{col 33}Minimum{col 44}Average{col 55}Maximum
{txt}{hline 16}{c +}{hline 44}
{col 12}{res}_all{col 17}{txt}{c |}{res}{col 21}       1{col 31}   15,048{col 42} 15,048.0{col 53}   15,048
{col 8}{res}statenum{col 17}{txt}{c |}{res}{col 21}      20{col 31}      341{col 42}    752.4{col 53}    1,273
{txt}{hline 16}{c BT}{hline 44}

{txt}Integration method: {col 25}{res}laplace

{col 49}{txt}Wald chi2({res}15{txt}){col 67}={res}{col 70}   624.27
{txt}Log likelihood = {res}-4913.8504{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}            polspon{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}oos_adopt25_d {c |}{col 21}{res}{space 2}  .283768{col 33}{space 2} .0987979{col 44}{space 1}    2.87{col 53}{space 3}0.004{col 61}{space 4} .0901276{col 74}{space 3} .4774083
{txt}{space 14}judic {c |}{col 21}{res}{space 2} .4430927{col 33}{space 2}  .064574{col 44}{space 1}    6.86{col 53}{space 3}0.000{col 61}{space 4} .3165299{col 74}{space 3} .5696555
{txt}{space 14}chair {c |}{col 21}{res}{space 2} .5476648{col 33}{space 2} .1949028{col 44}{space 1}    2.81{col 53}{space 3}0.005{col 61}{space 4} .1656623{col 74}{space 3} .9296673
{txt}{space 13}leader {c |}{col 21}{res}{space 2} .1618571{col 33}{space 2} .1022548{col 44}{space 1}    1.58{col 53}{space 3}0.113{col 61}{space 4}-.0385585{col 74}{space 3} .3622728
{txt}{space 13}female {c |}{col 21}{res}{space 2} .0260945{col 33}{space 2} .0666272{col 44}{space 1}    0.39{col 53}{space 3}0.695{col 61}{space 4}-.1044925{col 74}{space 3} .1566815
{txt}{space 12}majmemb {c |}{col 21}{res}{space 2} .2047677{col 33}{space 2} .0562325{col 44}{space 1}    3.64{col 53}{space 3}0.000{col 61}{space 4}  .094554{col 74}{space 3} .3149813
{txt}{space 11}blackpct {c |}{col 21}{res}{space 2}-.3011605{col 33}{space 2} .1837912{col 44}{space 1}   -1.64{col 53}{space 3}0.101{col 61}{space 4}-.6613846{col 74}{space 3} .0590636
{txt}{space 15}srty {c |}{col 21}{res}{space 2}-.0303781{col 33}{space 2} .0045595{col 44}{space 1}   -6.66{col 53}{space 3}0.000{col 61}{space 4}-.0393145{col 74}{space 3}-.0214417
{txt}{space 13}senate {c |}{col 21}{res}{space 2} .7581314{col 33}{space 2} .0701092{col 44}{space 1}   10.81{col 53}{space 3}0.000{col 61}{space 4} .6207199{col 74}{space 3} .8955429
{txt}{space 13}squire {c |}{col 21}{res}{space 2} 4.860881{col 33}{space 2} 1.585921{col 44}{space 1}    3.07{col 53}{space 3}0.002{col 61}{space 4} 1.752534{col 74}{space 3} 7.969229
{txt}{space 12}sponlim {c |}{col 21}{res}{space 2} .0748482{col 33}{space 2}   .21743{col 44}{space 1}    0.34{col 53}{space 3}0.731{col 61}{space 4}-.3513068{col 74}{space 3} .5010032
{txt}{space 12}termlim {c |}{col 21}{res}{space 2} .6894127{col 33}{space 2} .2170948{col 44}{space 1}    3.18{col 53}{space 3}0.001{col 61}{space 4} .2639147{col 74}{space 3} 1.114911
{txt}{space 12}polcons {c |}{col 21}{res}{space 2} 2.451629{col 33}{space 2} .8489306{col 44}{space 1}    2.89{col 53}{space 3}0.004{col 61}{space 4} .7877555{col 74}{space 3} 4.115502
{txt}{space 12}smideol {c |}{col 21}{res}{space 2}-2.186483{col 33}{space 2} .1354166{col 44}{space 1}  -16.15{col 53}{space 3}0.000{col 61}{space 4}-2.451894{col 74}{space 3}-1.921071
{txt}{space 19} {c |}
c.polcons#c.smideol {c |}{col 21}{res}{space 2}  4.74189{col 33}{space 2} .2626413{col 44}{space 1}   18.05{col 53}{space 3}0.000{col 61}{space 4} 4.227122{col 74}{space 3} 5.256657
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2}-4.771867{col 33}{space 2} .5360697{col 44}{space 1}   -8.90{col 53}{space 3}0.000{col 61}{space 4}-5.822544{col 74}{space 3} -3.72119
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}_all>polnum        {col 21}{txt}{c |}
{space 10}var(_cons){c |}{col 21}{res}{space 2}  .227118{col 33}{space 2}  .102403{col 61}{space 4} .0938559{col 74}{space 3} .5495933
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}statenum           {col 21}{txt}{c |}
{space 10}var(_cons){c |}{col 21}{res}{space 2} .6210599{col 33}{space 2}  .217744{col 61}{space 4} .3123932{col 74}{space 3} 1.234711
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}chi2({res}2{txt}) ={res} 556.22{col 59}{txt}Prob > chi2 ={res}{col 73}0.0000

{txt}{p 0 6 4 79}Note: {help j_mixedlr##|_new:LR test is conservative} and provided only for reference.{p_end}

{com}. 
. *Alternative definition of exposure district (Table A-5, Row 8)
. *Authorship model
. quietly melogit polauth oos_adopt_ms judic chair leader female majmemb blackpct srty senate squire introlim termlim c.polcons##c.smideol if multauth==1 || _all: R.polnum
{txt}
{com}. matrix m1 = e(b)
{txt}
{com}. melogit polauth oos_adopt_ms judic chair leader female majmemb blackpct srty senate squire introlim termlim c.polcons##c.smideol if multauth==1 || _all: R.polnum|| statenum:, from(m1) difficult
{res}{txt}{p 0 0 2}
note: crossed random-effects model specified; option intmethod(laplace) implied
{p_end}

Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-2814.0279}  
Iteration 1:{space 3}log likelihood = {res:-2548.4003}  
Iteration 2:{space 3}log likelihood = {res:-2538.2316}  
Iteration 3:{space 3}log likelihood = {res:-2538.1766}  
Iteration 4:{space 3}log likelihood = {res:-2538.1766}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-2401.8628}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-2401.8628}  (not concave)
Iteration 1:{space 3}log likelihood = {res:-2394.7366}  (not concave)
Iteration 2:{space 3}log likelihood = {res:-2386.9573}  
Iteration 3:{space 3}log likelihood = {res:-2384.9234}  
Iteration 4:{space 3}log likelihood = {res:-2384.9157}  (not concave)
Iteration 5:{space 3}log likelihood = {res:-2384.7936}  
Iteration 6:{space 3}log likelihood = {res:-2384.7319}  
Iteration 7:{space 3}log likelihood = {res:-2384.7273}  
Iteration 8:{space 3}log likelihood = {res:-2384.7271}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    10,834

{txt}{hline 16}{c TT}{hline 44}
{col 17}{txt}{c |}{col 23}No. of{col 36}Observations per Group
{col 2}{txt}Group Variable{col 17}{c |}{col 23}Groups{col 33}Minimum{col 44}Average{col 55}Maximum
{txt}{hline 16}{c +}{hline 44}
{col 12}{res}_all{col 17}{txt}{c |}{res}{col 21}       1{col 31}   10,834{col 42} 10,834.0{col 53}   10,834
{col 8}{res}statenum{col 17}{txt}{c |}{res}{col 21}      14{col 31}      343{col 42}    773.9{col 53}    1,273
{txt}{hline 16}{c BT}{hline 44}

{txt}Integration method: {col 25}{res}laplace

{col 49}{txt}Wald chi2({res}15{txt}){col 67}={res}{col 70}   309.59
{txt}Log likelihood = {res}-2384.7271{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}            polauth{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}oos_adopt_ms {c |}{col 21}{res}{space 2} .2667089{col 33}{space 2}   .12101{col 44}{space 1}    2.20{col 53}{space 3}0.028{col 61}{space 4} .0295336{col 74}{space 3} .5038843
{txt}{space 14}judic {c |}{col 21}{res}{space 2} .5808832{col 33}{space 2} .0957826{col 44}{space 1}    6.06{col 53}{space 3}0.000{col 61}{space 4} .3931527{col 74}{space 3} .7686136
{txt}{space 14}chair {c |}{col 21}{res}{space 2} .7579114{col 33}{space 2} .2585757{col 44}{space 1}    2.93{col 53}{space 3}0.003{col 61}{space 4} .2511125{col 74}{space 3}  1.26471
{txt}{space 13}leader {c |}{col 21}{res}{space 2} .1312754{col 33}{space 2} .1508614{col 44}{space 1}    0.87{col 53}{space 3}0.384{col 61}{space 4}-.1644074{col 74}{space 3} .4269583
{txt}{space 13}female {c |}{col 21}{res}{space 2} .1242073{col 33}{space 2} .0961464{col 44}{space 1}    1.29{col 53}{space 3}0.196{col 61}{space 4}-.0642362{col 74}{space 3} .3126507
{txt}{space 12}majmemb {c |}{col 21}{res}{space 2} .2476322{col 33}{space 2} .0878013{col 44}{space 1}    2.82{col 53}{space 3}0.005{col 61}{space 4} .0755448{col 74}{space 3} .4197196
{txt}{space 11}blackpct {c |}{col 21}{res}{space 2}-.1122467{col 33}{space 2} .2768547{col 44}{space 1}   -0.41{col 53}{space 3}0.685{col 61}{space 4} -.654872{col 74}{space 3} .4303787
{txt}{space 15}srty {c |}{col 21}{res}{space 2}-.0154043{col 33}{space 2} .0066869{col 44}{space 1}   -2.30{col 53}{space 3}0.021{col 61}{space 4}-.0285104{col 74}{space 3}-.0022982
{txt}{space 13}senate {c |}{col 21}{res}{space 2} .5892269{col 33}{space 2} .1019412{col 44}{space 1}    5.78{col 53}{space 3}0.000{col 61}{space 4} .3894258{col 74}{space 3}  .789028
{txt}{space 13}squire {c |}{col 21}{res}{space 2} 4.192763{col 33}{space 2} 1.883287{col 44}{space 1}    2.23{col 53}{space 3}0.026{col 61}{space 4} .5015878{col 74}{space 3} 7.883938
{txt}{space 11}introlim {c |}{col 21}{res}{space 2}-.9466831{col 33}{space 2} .5746014{col 44}{space 1}   -1.65{col 53}{space 3}0.099{col 61}{space 4}-2.072881{col 74}{space 3}  .179515
{txt}{space 12}termlim {c |}{col 21}{res}{space 2}  .350599{col 33}{space 2} .2340029{col 44}{space 1}    1.50{col 53}{space 3}0.134{col 61}{space 4}-.1080383{col 74}{space 3} .8092363
{txt}{space 12}polcons {c |}{col 21}{res}{space 2} 2.009587{col 33}{space 2} .9106763{col 44}{space 1}    2.21{col 53}{space 3}0.027{col 61}{space 4} .2246943{col 74}{space 3}  3.79448
{txt}{space 12}smideol {c |}{col 21}{res}{space 2}-2.278757{col 33}{space 2} .1989447{col 44}{space 1}  -11.45{col 53}{space 3}0.000{col 61}{space 4}-2.668682{col 74}{space 3}-1.888833
{txt}{space 19} {c |}
c.polcons#c.smideol {c |}{col 21}{res}{space 2} 5.023429{col 33}{space 2}  .386681{col 44}{space 1}   12.99{col 53}{space 3}0.000{col 61}{space 4} 4.265548{col 74}{space 3}  5.78131
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2}-5.164522{col 33}{space 2} .6130773{col 44}{space 1}   -8.42{col 53}{space 3}0.000{col 61}{space 4}-6.366132{col 74}{space 3}-3.962913
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}_all>polnum        {col 21}{txt}{c |}
{space 10}var(_cons){c |}{col 21}{res}{space 2}   .22079{col 33}{space 2} .1054298{col 61}{space 4} .0866005{col 74}{space 3} .5629091
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}statenum           {col 21}{txt}{c |}
{space 10}var(_cons){c |}{col 21}{res}{space 2} .4896768{col 33}{space 2} .2001577{col 61}{space 4} .2197743{col 74}{space 3} 1.091044
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}chi2({res}2{txt}) ={res} 306.90{col 59}{txt}Prob > chi2 ={res}{col 73}0.0000

{txt}{p 0 6 4 79}Note: {help j_mixedlr##|_new:LR test is conservative} and provided only for reference.{p_end}

{com}. *Sponsorship model
. quietly melogit polspon oos_adopt_ms judic chair leader female majmemb blackpct srty senate squire sponlim termlim c.polcons##c.smideol || _all: R.polnum
{txt}
{com}. matrix m2 = e(b)
{txt}
{com}. melogit polspon oos_adopt_ms judic chair leader female majmemb blackpct srty senate squire sponlim termlim c.polcons##c.smideol || _all: R.polnum|| statenum:, from(m2) difficult
{res}{txt}{p 0 0 2}
note: crossed random-effects model specified; option intmethod(laplace) implied
{p_end}

Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-5376.1198}  
Iteration 1:{space 3}log likelihood = {res:-5194.8702}  
Iteration 2:{space 3}log likelihood = {res:-5192.0962}  
Iteration 3:{space 3}log likelihood = {res:-5192.0943}  
Iteration 4:{space 3}log likelihood = {res:-5192.0943}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-4948.3605}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-4948.3605}  (not concave)
Iteration 1:{space 3}log likelihood = {res:-4913.9199}  (not concave)
Iteration 2:{space 3}log likelihood = {res:-4913.5188}  
Iteration 3:{space 3}log likelihood = {res:-4913.3592}  
Iteration 4:{space 3}log likelihood = {res:-4913.3526}  
Iteration 5:{space 3}log likelihood = {res:-4913.3513}  
Iteration 6:{space 3}log likelihood = {res:-4913.3513}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    15,048

{txt}{hline 16}{c TT}{hline 44}
{col 17}{txt}{c |}{col 23}No. of{col 36}Observations per Group
{col 2}{txt}Group Variable{col 17}{c |}{col 23}Groups{col 33}Minimum{col 44}Average{col 55}Maximum
{txt}{hline 16}{c +}{hline 44}
{col 12}{res}_all{col 17}{txt}{c |}{res}{col 21}       1{col 31}   15,048{col 42} 15,048.0{col 53}   15,048
{col 8}{res}statenum{col 17}{txt}{c |}{res}{col 21}      20{col 31}      341{col 42}    752.4{col 53}    1,273
{txt}{hline 16}{c BT}{hline 44}

{txt}Integration method: {col 25}{res}laplace

{col 49}{txt}Wald chi2({res}15{txt}){col 67}={res}{col 70}   637.59
{txt}Log likelihood = {res}-4913.3513{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}            polspon{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}oos_adopt_ms {c |}{col 21}{res}{space 2} .2603334{col 33}{space 2} .0858441{col 44}{space 1}    3.03{col 53}{space 3}0.002{col 61}{space 4} .0920821{col 74}{space 3} .4285847
{txt}{space 14}judic {c |}{col 21}{res}{space 2} .4450287{col 33}{space 2} .0645407{col 44}{space 1}    6.90{col 53}{space 3}0.000{col 61}{space 4} .3185312{col 74}{space 3} .5715262
{txt}{space 14}chair {c |}{col 21}{res}{space 2} .5511629{col 33}{space 2} .1949666{col 44}{space 1}    2.83{col 53}{space 3}0.005{col 61}{space 4} .1690354{col 74}{space 3} .9332904
{txt}{space 13}leader {c |}{col 21}{res}{space 2} .1655315{col 33}{space 2} .1023821{col 44}{space 1}    1.62{col 53}{space 3}0.106{col 61}{space 4}-.0351339{col 74}{space 3} .3661968
{txt}{space 13}female {c |}{col 21}{res}{space 2} .0279192{col 33}{space 2} .0666286{col 44}{space 1}    0.42{col 53}{space 3}0.675{col 61}{space 4}-.1026705{col 74}{space 3} .1585089
{txt}{space 12}majmemb {c |}{col 21}{res}{space 2} .2012903{col 33}{space 2} .0562088{col 44}{space 1}    3.58{col 53}{space 3}0.000{col 61}{space 4} .0911231{col 74}{space 3} .3114575
{txt}{space 11}blackpct {c |}{col 21}{res}{space 2}-.2802136{col 33}{space 2} .1834771{col 44}{space 1}   -1.53{col 53}{space 3}0.127{col 61}{space 4}-.6398222{col 74}{space 3} .0793949
{txt}{space 15}srty {c |}{col 21}{res}{space 2}-.0305274{col 33}{space 2} .0045688{col 44}{space 1}   -6.68{col 53}{space 3}0.000{col 61}{space 4}-.0394821{col 74}{space 3}-.0215727
{txt}{space 13}senate {c |}{col 21}{res}{space 2} .7589793{col 33}{space 2}  .070196{col 44}{space 1}   10.81{col 53}{space 3}0.000{col 61}{space 4} .6213978{col 74}{space 3} .8965609
{txt}{space 13}squire {c |}{col 21}{res}{space 2} 4.860993{col 33}{space 2} 1.582231{col 44}{space 1}    3.07{col 53}{space 3}0.002{col 61}{space 4} 1.759876{col 74}{space 3} 7.962109
{txt}{space 12}sponlim {c |}{col 21}{res}{space 2} .0863063{col 33}{space 2} .2173153{col 44}{space 1}    0.40{col 53}{space 3}0.691{col 61}{space 4}-.3396238{col 74}{space 3} .5122365
{txt}{space 12}termlim {c |}{col 21}{res}{space 2} .6960716{col 33}{space 2} .2167909{col 44}{space 1}    3.21{col 53}{space 3}0.001{col 61}{space 4} .2711692{col 74}{space 3} 1.120974
{txt}{space 12}polcons {c |}{col 21}{res}{space 2}  2.44695{col 33}{space 2} .8455528{col 44}{space 1}    2.89{col 53}{space 3}0.004{col 61}{space 4}  .789697{col 74}{space 3} 4.104203
{txt}{space 12}smideol {c |}{col 21}{res}{space 2}-2.188365{col 33}{space 2} .1325068{col 44}{space 1}  -16.52{col 53}{space 3}0.000{col 61}{space 4}-2.448073{col 74}{space 3}-1.928656
{txt}{space 19} {c |}
c.polcons#c.smideol {c |}{col 21}{res}{space 2}  4.74682{col 33}{space 2} .2569585{col 44}{space 1}   18.47{col 53}{space 3}0.000{col 61}{space 4}  4.24319{col 74}{space 3} 5.250449
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2}-4.782262{col 33}{space 2} .5340044{col 44}{space 1}   -8.96{col 53}{space 3}0.000{col 61}{space 4}-5.828891{col 74}{space 3}-3.735633
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}_all>polnum        {col 21}{txt}{c |}
{space 10}var(_cons){c |}{col 21}{res}{space 2} .2252335{col 33}{space 2} .1017212{col 61}{space 4} .0929412{col 74}{space 3} .5458301
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}statenum           {col 21}{txt}{c |}
{space 10}var(_cons){c |}{col 21}{res}{space 2} .6204017{col 33}{space 2} .2169139{col 61}{space 4} .3126536{col 74}{space 3} 1.231069
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}chi2({res}2{txt}) ={res} 557.49{col 59}{txt}Prob > chi2 ={res}{col 73}0.0000

{txt}{p 0 6 4 79}Note: {help j_mixedlr##|_new:LR test is conservative} and provided only for reference.{p_end}

{com}. 
. *Excluding multistate districts with mixed adoptions (Table A-5, Row 9)
. *Authorship model
. quietly melogit polauth oos_adopt_d judic chair leader female majmemb blackpct srty senate squire introlim termlim c.polcons##c.smideol if multauth==1 & multistate_adopt!=1 || _all: R.polnum
{txt}
{com}. matrix g1 = e(b)
{txt}
{com}. melogit polauth oos_adopt_d judic chair leader female majmemb blackpct srty senate squire introlim termlim c.polcons##c.smideol if multauth==1 & multistate_adopt!=1 || _all: R.polnum|| statenum:, from(g1) difficult
{res}{txt}{p 0 0 2}
note: crossed random-effects model specified; option intmethod(laplace) implied
{p_end}

Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-2815.2271}  
Iteration 1:{space 3}log likelihood = {res: -2557.929}  
Iteration 2:{space 3}log likelihood = {res:-2547.6885}  
Iteration 3:{space 3}log likelihood = {res:-2547.6083}  
Iteration 4:{space 3}log likelihood = {res:-2547.6082}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-2396.5756}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-2396.5756}  (not concave)
Iteration 1:{space 3}log likelihood = {res:-2392.1037}  (not concave)
Iteration 2:{space 3}log likelihood = {res:-2384.2835}  (not concave)
Iteration 3:{space 3}log likelihood = {res:-2383.5756}  (not concave)
Iteration 4:{space 3}log likelihood = {res:-2383.0572}  
Iteration 5:{space 3}log likelihood = {res:-2381.8955}  
Iteration 6:{space 3}log likelihood = {res:-2381.8835}  
Iteration 7:{space 3}log likelihood = {res:-2381.8816}  
Iteration 8:{space 3}log likelihood = {res:-2381.8815}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    10,798

{txt}{hline 16}{c TT}{hline 44}
{col 17}{txt}{c |}{col 23}No. of{col 36}Observations per Group
{col 2}{txt}Group Variable{col 17}{c |}{col 23}Groups{col 33}Minimum{col 44}Average{col 55}Maximum
{txt}{hline 16}{c +}{hline 44}
{col 12}{res}_all{col 17}{txt}{c |}{res}{col 21}       1{col 31}   10,798{col 42} 10,798.0{col 53}   10,798
{col 8}{res}statenum{col 17}{txt}{c |}{res}{col 21}      14{col 31}      337{col 42}    771.3{col 53}    1,273
{txt}{hline 16}{c BT}{hline 44}

{txt}Integration method: {col 25}{res}laplace

{col 49}{txt}Wald chi2({res}15{txt}){col 67}={res}{col 70}   321.01
{txt}Log likelihood = {res}-2381.8815{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}            polauth{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}oos_adopt_d {c |}{col 21}{res}{space 2} .3654866{col 33}{space 2} .1387551{col 44}{space 1}    2.63{col 53}{space 3}0.008{col 61}{space 4} .0935316{col 74}{space 3} .6374416
{txt}{space 14}judic {c |}{col 21}{res}{space 2} .5782915{col 33}{space 2} .0958114{col 44}{space 1}    6.04{col 53}{space 3}0.000{col 61}{space 4} .3905046{col 74}{space 3} .7660785
{txt}{space 14}chair {c |}{col 21}{res}{space 2} .7540009{col 33}{space 2}  .258516{col 44}{space 1}    2.92{col 53}{space 3}0.004{col 61}{space 4}  .247319{col 74}{space 3} 1.260683
{txt}{space 13}leader {c |}{col 21}{res}{space 2}  .130067{col 33}{space 2} .1508321{col 44}{space 1}    0.86{col 53}{space 3}0.389{col 61}{space 4}-.1655584{col 74}{space 3} .4256924
{txt}{space 13}female {c |}{col 21}{res}{space 2}  .120591{col 33}{space 2} .0960517{col 44}{space 1}    1.26{col 53}{space 3}0.209{col 61}{space 4} -.067667{col 74}{space 3} .3088489
{txt}{space 12}majmemb {c |}{col 21}{res}{space 2} .2579799{col 33}{space 2} .0885184{col 44}{space 1}    2.91{col 53}{space 3}0.004{col 61}{space 4}  .084487{col 74}{space 3} .4314728
{txt}{space 11}blackpct {c |}{col 21}{res}{space 2}-.1514509{col 33}{space 2} .2774394{col 44}{space 1}   -0.55{col 53}{space 3}0.585{col 61}{space 4}-.6952222{col 74}{space 3} .3923204
{txt}{space 15}srty {c |}{col 21}{res}{space 2}-.0155548{col 33}{space 2} .0066964{col 44}{space 1}   -2.32{col 53}{space 3}0.020{col 61}{space 4}-.0286796{col 74}{space 3}-.0024301
{txt}{space 13}senate {c |}{col 21}{res}{space 2} .5925731{col 33}{space 2} .1020074{col 44}{space 1}    5.81{col 53}{space 3}0.000{col 61}{space 4} .3926422{col 74}{space 3} .7925039
{txt}{space 13}squire {c |}{col 21}{res}{space 2} 4.177724{col 33}{space 2}  1.89605{col 44}{space 1}    2.20{col 53}{space 3}0.028{col 61}{space 4} .4615352{col 74}{space 3} 7.893914
{txt}{space 11}introlim {c |}{col 21}{res}{space 2}-.9760214{col 33}{space 2} .5785396{col 44}{space 1}   -1.69{col 53}{space 3}0.092{col 61}{space 4}-2.109938{col 74}{space 3} .1578953
{txt}{space 12}termlim {c |}{col 21}{res}{space 2} .3284147{col 33}{space 2}  .234938{col 44}{space 1}    1.40{col 53}{space 3}0.162{col 61}{space 4}-.1320554{col 74}{space 3} .7888848
{txt}{space 12}polcons {c |}{col 21}{res}{space 2} 2.012809{col 33}{space 2}   .93114{col 44}{space 1}    2.16{col 53}{space 3}0.031{col 61}{space 4} .1878079{col 74}{space 3}  3.83781
{txt}{space 12}smideol {c |}{col 21}{res}{space 2}-2.282737{col 33}{space 2} .1948758{col 44}{space 1}  -11.71{col 53}{space 3}0.000{col 61}{space 4}-2.664686{col 74}{space 3}-1.900787
{txt}{space 19} {c |}
c.polcons#c.smideol {c |}{col 21}{res}{space 2} 5.032618{col 33}{space 2} .3792514{col 44}{space 1}   13.27{col 53}{space 3}0.000{col 61}{space 4} 4.289299{col 74}{space 3} 5.775937
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2}-5.153296{col 33}{space 2} .6209649{col 44}{space 1}   -8.30{col 53}{space 3}0.000{col 61}{space 4}-6.370365{col 74}{space 3}-3.936227
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}_all>polnum        {col 21}{txt}{c |}
{space 10}var(_cons){c |}{col 21}{res}{space 2}  .233265{col 33}{space 2} .1110682{col 61}{space 4} .0917389{col 74}{space 3} .5931243
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}statenum           {col 21}{txt}{c |}
{space 10}var(_cons){c |}{col 21}{res}{space 2} .4975984{col 33}{space 2} .2028189{col 61}{space 4} .2238376{col 74}{space 3} 1.106178
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}chi2({res}2{txt}) ={res} 331.45{col 59}{txt}Prob > chi2 ={res}{col 73}0.0000

{txt}{p 0 6 4 79}Note: {help j_mixedlr##|_new:LR test is conservative} and provided only for reference.{p_end}

{com}. *Sponsorship model
. quietly melogit polspon oos_adopt_d judic chair leader female majmemb blackpct srty senate squire sponlim termlim c.polcons##c.smideol if multistate_adopt!=1 || _all: R.polnum
{txt}
{com}. matrix h1 = e(b)
{txt}
{com}. melogit polspon oos_adopt_d judic chair leader female majmemb blackpct srty senate squire sponlim termlim c.polcons##c.smideol if multistate_adopt!=1 || _all: R.polnum|| statenum:, from(h1) difficult
{res}{txt}{p 0 0 2}
note: crossed random-effects model specified; option intmethod(laplace) implied
{p_end}

Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-5352.2073}  
Iteration 1:{space 3}log likelihood = {res:-5171.5224}  
Iteration 2:{space 3}log likelihood = {res:-5168.7559}  
Iteration 3:{space 3}log likelihood = {res: -5168.754}  
Iteration 4:{space 3}log likelihood = {res: -5168.754}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-4928.3227}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-4928.3227}  (not concave)
Iteration 1:{space 3}log likelihood = {res: -4894.317}  (not concave)
Iteration 2:{space 3}log likelihood = {res:-4894.0389}  (not concave)
Iteration 3:{space 3}log likelihood = {res:-4892.8964}  
Iteration 4:{space 3}log likelihood = {res:-4892.8452}  (not concave)
Iteration 5:{space 3}log likelihood = {res:-4892.7883}  
Iteration 6:{space 3}log likelihood = {res:-4892.7822}  
Iteration 7:{space 3}log likelihood = {res:-4892.7822}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    14,999

{txt}{hline 16}{c TT}{hline 44}
{col 17}{txt}{c |}{col 23}No. of{col 36}Observations per Group
{col 2}{txt}Group Variable{col 17}{c |}{col 23}Groups{col 33}Minimum{col 44}Average{col 55}Maximum
{txt}{hline 16}{c +}{hline 44}
{col 12}{res}_all{col 17}{txt}{c |}{res}{col 21}       1{col 31}   14,999{col 42} 14,999.0{col 53}   14,999
{col 8}{res}statenum{col 17}{txt}{c |}{res}{col 21}      20{col 31}      337{col 42}    750.0{col 53}    1,273
{txt}{hline 16}{c BT}{hline 44}

{txt}Integration method: {col 25}{res}laplace

{col 49}{txt}Wald chi2({res}15{txt}){col 67}={res}{col 70}   622.02
{txt}Log likelihood = {res}-4892.7822{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}            polspon{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}oos_adopt_d {c |}{col 21}{res}{space 2} .3462068{col 33}{space 2} .0946801{col 44}{space 1}    3.66{col 53}{space 3}0.000{col 61}{space 4} .1606372{col 74}{space 3} .5317763
{txt}{space 14}judic {c |}{col 21}{res}{space 2} .4440978{col 33}{space 2} .0646848{col 44}{space 1}    6.87{col 53}{space 3}0.000{col 61}{space 4}  .317318{col 74}{space 3} .5708776
{txt}{space 14}chair {c |}{col 21}{res}{space 2} .5518727{col 33}{space 2} .1950592{col 44}{space 1}    2.83{col 53}{space 3}0.005{col 61}{space 4} .1695636{col 74}{space 3} .9341818
{txt}{space 13}leader {c |}{col 21}{res}{space 2}  .152853{col 33}{space 2}  .102552{col 44}{space 1}    1.49{col 53}{space 3}0.136{col 61}{space 4}-.0481452{col 74}{space 3} .3538512
{txt}{space 13}female {c |}{col 21}{res}{space 2} .0285226{col 33}{space 2} .0667223{col 44}{space 1}    0.43{col 53}{space 3}0.669{col 61}{space 4}-.1022508{col 74}{space 3}  .159296
{txt}{space 12}majmemb {c |}{col 21}{res}{space 2} .2052341{col 33}{space 2} .0562454{col 44}{space 1}    3.65{col 53}{space 3}0.000{col 61}{space 4} .0949951{col 74}{space 3} .3154731
{txt}{space 11}blackpct {c |}{col 21}{res}{space 2}-.3227532{col 33}{space 2} .1844352{col 44}{space 1}   -1.75{col 53}{space 3}0.080{col 61}{space 4}-.6842395{col 74}{space 3} .0387331
{txt}{space 15}srty {c |}{col 21}{res}{space 2}-.0303039{col 33}{space 2} .0045611{col 44}{space 1}   -6.64{col 53}{space 3}0.000{col 61}{space 4}-.0392435{col 74}{space 3}-.0213643
{txt}{space 13}senate {c |}{col 21}{res}{space 2} .7486023{col 33}{space 2} .0704397{col 44}{space 1}   10.63{col 53}{space 3}0.000{col 61}{space 4}  .610543{col 74}{space 3} .8866616
{txt}{space 13}squire {c |}{col 21}{res}{space 2} 5.078278{col 33}{space 2} 1.596438{col 44}{space 1}    3.18{col 53}{space 3}0.001{col 61}{space 4} 1.949317{col 74}{space 3} 8.207239
{txt}{space 12}sponlim {c |}{col 21}{res}{space 2} .0701012{col 33}{space 2} .2171879{col 44}{space 1}    0.32{col 53}{space 3}0.747{col 61}{space 4}-.3555792{col 74}{space 3} .4957816
{txt}{space 12}termlim {c |}{col 21}{res}{space 2} .6722039{col 33}{space 2} .2177393{col 44}{space 1}    3.09{col 53}{space 3}0.002{col 61}{space 4} .2454428{col 74}{space 3} 1.098965
{txt}{space 12}polcons {c |}{col 21}{res}{space 2}  2.47788{col 33}{space 2} .9098045{col 44}{space 1}    2.72{col 53}{space 3}0.006{col 61}{space 4} .6946958{col 74}{space 3} 4.261064
{txt}{space 12}smideol {c |}{col 21}{res}{space 2}-2.185926{col 33}{space 2} .1343926{col 44}{space 1}  -16.27{col 53}{space 3}0.000{col 61}{space 4}-2.449331{col 74}{space 3}-1.922522
{txt}{space 19} {c |}
c.polcons#c.smideol {c |}{col 21}{res}{space 2} 4.739022{col 33}{space 2} .2597725{col 44}{space 1}   18.24{col 53}{space 3}0.000{col 61}{space 4} 4.229877{col 74}{space 3} 5.248167
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2}  -4.8211{col 33}{space 2} .5614556{col 44}{space 1}   -8.59{col 53}{space 3}0.000{col 61}{space 4}-5.921533{col 74}{space 3}-3.720667
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}_all>polnum        {col 21}{txt}{c |}
{space 10}var(_cons){c |}{col 21}{res}{space 2} .2214914{col 33}{space 2} .0998638{col 61}{space 4} .0915326{col 74}{space 3} .5359667
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}statenum           {col 21}{txt}{c |}
{space 10}var(_cons){c |}{col 21}{res}{space 2} .6190291{col 33}{space 2} .2175716{col 61}{space 4} .3108403{col 74}{space 3} 1.232778
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}chi2({res}2{txt}) ={res} 551.94{col 59}{txt}Prob > chi2 ={res}{col 73}0.0000

{txt}{p 0 6 4 79}Note: {help j_mixedlr##|_new:LR test is conservative} and provided only for reference.{p_end}

{com}. 
. *Alternative measure of exposure districts (Table A-5, Rows 10 and 11)   
. *Authorship model
. quietly melogit polauth i.oos_adopt_full judic chair leader female majmemb blackpct srty senate squire introlim termlim c.polcons##c.smideol if multauth==1 || _all: R.polnum
{txt}
{com}. matrix d1 = e(b)
{txt}
{com}. melogit polauth i.oos_adopt_full judic chair leader female majmemb blackpct srty senate squire introlim termlim c.polcons##c.smideol  if multauth==1 || _all: R.polnum|| statenum:, from(d1) difficult
{res}{txt}{p 0 0 2}
note: crossed random-effects model specified; option intmethod(laplace) implied
{p_end}

Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-2820.1012}  
Iteration 1:{space 3}log likelihood = {res:-2562.1122}  
Iteration 2:{space 3}log likelihood = {res:-2551.8342}  
Iteration 3:{space 3}log likelihood = {res:-2551.7529}  
Iteration 4:{space 3}log likelihood = {res:-2551.7528}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res: -2399.295}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res: -2399.295}  (not concave)
Iteration 1:{space 3}log likelihood = {res:-2394.9222}  (not concave)
Iteration 2:{space 3}log likelihood = {res:-2386.8738}  (not concave)
Iteration 3:{space 3}log likelihood = {res:-2384.5281}  (not concave)
Iteration 4:{space 3}log likelihood = {res:-2384.4856}  
Iteration 5:{space 3}log likelihood = {res:-2384.3887}  
Iteration 6:{space 3}log likelihood = {res:-2384.3831}  
Iteration 7:{space 3}log likelihood = {res:-2384.3825}  
Iteration 8:{space 3}log likelihood = {res:-2384.3825}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    10,834

{txt}{hline 16}{c TT}{hline 44}
{col 17}{txt}{c |}{col 23}No. of{col 36}Observations per Group
{col 2}{txt}Group Variable{col 17}{c |}{col 23}Groups{col 33}Minimum{col 44}Average{col 55}Maximum
{txt}{hline 16}{c +}{hline 44}
{col 12}{res}_all{col 17}{txt}{c |}{res}{col 21}       1{col 31}   10,834{col 42} 10,834.0{col 53}   10,834
{col 8}{res}statenum{col 17}{txt}{c |}{res}{col 21}      14{col 31}      343{col 42}    773.9{col 53}    1,273
{txt}{hline 16}{c BT}{hline 44}

{txt}Integration method: {col 25}{res}laplace

{col 49}{txt}Wald chi2({res}16{txt}){col 67}={res}{col 70}   305.87
{txt}Log likelihood = {res}-2384.3825{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                polauth{col 25}{c |}      Coef.{col 37}   Std. Err.{col 49}      z{col 57}   P>|z|{col 65}     [95% Con{col 78}f. Interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}oos_adopt_full {c |}
Partially out-of state  {c |}{col 25}{res}{space 2} .3575923{col 37}{space 2} .1757489{col 48}{space 1}    2.03{col 57}{space 3}0.042{col 65}{space 4} .0131308{col 78}{space 3} .7020539
{txt}{space 4}Fully out-of-state  {c |}{col 25}{res}{space 2} .2872617{col 37}{space 2} .2045065{col 48}{space 1}    1.40{col 57}{space 3}0.160{col 65}{space 4}-.1135636{col 78}{space 3} .6880871
{txt}{space 23} {c |}
{space 18}judic {c |}{col 25}{res}{space 2} .5781705{col 37}{space 2}  .095808{col 48}{space 1}    6.03{col 57}{space 3}0.000{col 65}{space 4} .3903903{col 78}{space 3} .7659507
{txt}{space 18}chair {c |}{col 25}{res}{space 2} .7586918{col 37}{space 2}    .2586{col 48}{space 1}    2.93{col 57}{space 3}0.003{col 65}{space 4} .2518452{col 78}{space 3} 1.265538
{txt}{space 17}leader {c |}{col 25}{res}{space 2} .1308894{col 37}{space 2} .1506359{col 48}{space 1}    0.87{col 57}{space 3}0.385{col 65}{space 4}-.1643516{col 78}{space 3} .4261304
{txt}{space 17}female {c |}{col 25}{res}{space 2} .1252297{col 37}{space 2} .0962086{col 48}{space 1}    1.30{col 57}{space 3}0.193{col 65}{space 4}-.0633357{col 78}{space 3}  .313795
{txt}{space 16}majmemb {c |}{col 25}{res}{space 2} .2566853{col 37}{space 2} .0883983{col 48}{space 1}    2.90{col 57}{space 3}0.004{col 65}{space 4} .0834278{col 78}{space 3} .4299428
{txt}{space 15}blackpct {c |}{col 25}{res}{space 2}-.1685239{col 37}{space 2} .2776929{col 48}{space 1}   -0.61{col 57}{space 3}0.544{col 65}{space 4} -.712792{col 78}{space 3} .3757442
{txt}{space 19}srty {c |}{col 25}{res}{space 2}-.0153509{col 37}{space 2} .0066828{col 48}{space 1}   -2.30{col 57}{space 3}0.022{col 65}{space 4} -.028449{col 78}{space 3}-.0022527
{txt}{space 17}senate {c |}{col 25}{res}{space 2}  .586638{col 37}{space 2} .1021456{col 48}{space 1}    5.74{col 57}{space 3}0.000{col 65}{space 4} .3864363{col 78}{space 3} .7868397
{txt}{space 17}squire {c |}{col 25}{res}{space 2} 4.156765{col 37}{space 2} 1.901926{col 48}{space 1}    2.19{col 57}{space 3}0.029{col 65}{space 4} .4290593{col 78}{space 3} 7.884472
{txt}{space 15}introlim {c |}{col 25}{res}{space 2}-.9690807{col 37}{space 2} .5806245{col 48}{space 1}   -1.67{col 57}{space 3}0.095{col 65}{space 4}-2.107084{col 78}{space 3} .1689225
{txt}{space 16}termlim {c |}{col 25}{res}{space 2}  .340999{col 37}{space 2} .2343889{col 48}{space 1}    1.45{col 57}{space 3}0.146{col 65}{space 4}-.1183949{col 78}{space 3} .8003929
{txt}{space 16}polcons {c |}{col 25}{res}{space 2} 2.005969{col 37}{space 2} .9329627{col 48}{space 1}    2.15{col 57}{space 3}0.032{col 65}{space 4} .1773956{col 78}{space 3} 3.834542
{txt}{space 16}smideol {c |}{col 25}{res}{space 2}-2.285408{col 37}{space 2} .2031532{col 48}{space 1}  -11.25{col 57}{space 3}0.000{col 65}{space 4}-2.683581{col 78}{space 3}-1.887235
{txt}{space 23} {c |}
{space 4}c.polcons#c.smideol {c |}{col 25}{res}{space 2} 5.035649{col 37}{space 2}   .39534{col 48}{space 1}   12.74{col 57}{space 3}0.000{col 65}{space 4} 4.260797{col 78}{space 3} 5.810502
{txt}{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2}-5.148804{col 37}{space 2} .6227008{col 48}{space 1}   -8.27{col 57}{space 3}0.000{col 65}{space 4}-6.369275{col 78}{space 3}-3.928333
{txt}{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}_all>polnum            {col 25}{txt}{c |}
{space 14}var(_cons){c |}{col 25}{res}{space 2} .2341999{col 37}{space 2} .1111818{col 65}{space 4} .0923624{col 78}{space 3} .5938518
{txt}{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}statenum               {col 25}{txt}{c |}
{space 14}var(_cons){c |}{col 25}{res}{space 2} .5015338{col 37}{space 2}  .204193{col 65}{space 4} .2258106{col 78}{space 3} 1.113925
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}chi2({res}2{txt}) ={res} 334.74{col 59}{txt}Prob > chi2 ={res}{col 73}0.0000

{txt}{p 0 6 4 79}Note: {help j_mixedlr##|_new:LR test is conservative} and provided only for reference.{p_end}

{com}. *Sponsorship model
. quietly melogit polspon i.oos_adopt_full judic chair leader female majmemb blackpct srty senate squire sponlim termlim c.polcons##c.smideol || _all: R.polnum
{txt}
{com}. matrix d2 = e(b)
{txt}
{com}. melogit polspon i.oos_adopt_full judic chair leader female majmemb blackpct srty senate squire sponlim termlim c.polcons##c.smideol || _all: R.polnum|| statenum:, from(d2) difficult
{res}{txt}{p 0 0 2}
note: crossed random-effects model specified; option intmethod(laplace) implied
{p_end}

Fitting fixed-effects model:

Iteration 0:{space 3}log likelihood = {res:-5372.7252}  
Iteration 1:{space 3}log likelihood = {res:-5191.5038}  
Iteration 2:{space 3}log likelihood = {res: -5188.729}  
Iteration 3:{space 3}log likelihood = {res:-5188.7271}  
Iteration 4:{space 3}log likelihood = {res:-5188.7271}  

Refining starting values:

Grid node 0:{space 3}log likelihood = {res:-4947.6106}

Fitting full model:
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-4947.6106}  (not concave)
Iteration 1:{space 3}log likelihood = {res:-4912.0759}  (not concave)
Iteration 2:{space 3}log likelihood = {res:-4911.6815}  
Iteration 3:{space 3}log likelihood = {res:-4911.5559}  
Iteration 4:{space 3}log likelihood = {res: -4911.538}  
Iteration 5:{space 3}log likelihood = {res:-4911.5341}  
Iteration 6:{space 3}log likelihood = {res:-4911.5312}  
Iteration 7:{space 3}log likelihood = {res:-4911.5311}  
{res}
{txt}Mixed-effects logistic regression{col 49}{txt}Number of obs{col 67}={res}{col 69}    15,048

{txt}{hline 16}{c TT}{hline 44}
{col 17}{txt}{c |}{col 23}No. of{col 36}Observations per Group
{col 2}{txt}Group Variable{col 17}{c |}{col 23}Groups{col 33}Minimum{col 44}Average{col 55}Maximum
{txt}{hline 16}{c +}{hline 44}
{col 12}{res}_all{col 17}{txt}{c |}{res}{col 21}       1{col 31}   15,048{col 42} 15,048.0{col 53}   15,048
{col 8}{res}statenum{col 17}{txt}{c |}{res}{col 21}      20{col 31}      341{col 42}    752.4{col 53}    1,273
{txt}{hline 16}{c BT}{hline 44}

{txt}Integration method: {col 25}{res}laplace

{col 49}{txt}Wald chi2({res}16{txt}){col 67}={res}{col 70}   639.82
{txt}Log likelihood = {res}-4911.5311{col 49}{txt}Prob > chi2{col 67}={res}{col 73}0.0000
{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                polspon{col 25}{c |}      Coef.{col 37}   Std. Err.{col 49}      z{col 57}   P>|z|{col 65}     [95% Con{col 78}f. Interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}oos_adopt_full {c |}
Partially out-of state  {c |}{col 25}{res}{space 2} .2632714{col 37}{space 2} .1266841{col 48}{space 1}    2.08{col 57}{space 3}0.038{col 65}{space 4} .0149752{col 78}{space 3} .5115675
{txt}{space 4}Fully out-of-state  {c |}{col 25}{res}{space 2} .3955495{col 37}{space 2} .1267043{col 48}{space 1}    3.12{col 57}{space 3}0.002{col 65}{space 4} .1472136{col 78}{space 3} .6438854
{txt}{space 23} {c |}
{space 18}judic {c |}{col 25}{res}{space 2} .4442516{col 37}{space 2}  .064586{col 48}{space 1}    6.88{col 57}{space 3}0.000{col 65}{space 4} .3176653{col 78}{space 3} .5708378
{txt}{space 18}chair {c |}{col 25}{res}{space 2}  .544667{col 37}{space 2} .1950447{col 48}{space 1}    2.79{col 57}{space 3}0.005{col 65}{space 4} .1623864{col 78}{space 3} .9269476
{txt}{space 17}leader {c |}{col 25}{res}{space 2} .1636998{col 37}{space 2} .1023144{col 48}{space 1}    1.60{col 57}{space 3}0.110{col 65}{space 4}-.0368328{col 78}{space 3} .3642324
{txt}{space 17}female {c |}{col 25}{res}{space 2} .0268464{col 37}{space 2} .0666928{col 48}{space 1}    0.40{col 57}{space 3}0.687{col 65}{space 4} -.103869{col 78}{space 3} .1575619
{txt}{space 16}majmemb {c |}{col 25}{res}{space 2} .2070685{col 37}{space 2} .0562288{col 48}{space 1}    3.68{col 57}{space 3}0.000{col 65}{space 4}  .096862{col 78}{space 3} .3172749
{txt}{space 15}blackpct {c |}{col 25}{res}{space 2}-.2961855{col 37}{space 2} .1835552{col 48}{space 1}   -1.61{col 57}{space 3}0.107{col 65}{space 4} -.655947{col 78}{space 3} .0635761
{txt}{space 19}srty {c |}{col 25}{res}{space 2}-.0304187{col 37}{space 2}   .00456{col 48}{space 1}   -6.67{col 57}{space 3}0.000{col 65}{space 4}-.0393561{col 78}{space 3}-.0214813
{txt}{space 17}senate {c |}{col 25}{res}{space 2} .7563738{col 37}{space 2} .0702286{col 48}{space 1}   10.77{col 57}{space 3}0.000{col 65}{space 4} .6187282{col 78}{space 3} .8940193
{txt}{space 17}squire {c |}{col 25}{res}{space 2} 4.882838{col 37}{space 2} 1.582559{col 48}{space 1}    3.09{col 57}{space 3}0.002{col 65}{space 4} 1.781079{col 78}{space 3} 7.984598
{txt}{space 16}sponlim {c |}{col 25}{res}{space 2} .0800927{col 37}{space 2} .2170707{col 48}{space 1}    0.37{col 57}{space 3}0.712{col 65}{space 4} -.345358{col 78}{space 3} .5055434
{txt}{space 16}termlim {c |}{col 25}{res}{space 2} .6885844{col 37}{space 2} .2170288{col 48}{space 1}    3.17{col 57}{space 3}0.002{col 65}{space 4} .2632157{col 78}{space 3} 1.113953
{txt}{space 16}polcons {c |}{col 25}{res}{space 2} 2.455717{col 37}{space 2} .8451832{col 48}{space 1}    2.91{col 57}{space 3}0.004{col 65}{space 4} .7991881{col 78}{space 3} 4.112246
{txt}{space 16}smideol {c |}{col 25}{res}{space 2}-2.188513{col 37}{space 2} .1326949{col 48}{space 1}  -16.49{col 57}{space 3}0.000{col 65}{space 4} -2.44859{col 78}{space 3}-1.928435
{txt}{space 23} {c |}
{space 4}c.polcons#c.smideol {c |}{col 25}{res}{space 2} 4.747909{col 37}{space 2} .2572484{col 48}{space 1}   18.46{col 57}{space 3}0.000{col 65}{space 4} 4.243712{col 78}{space 3} 5.252107
{txt}{space 23} {c |}
{space 18}_cons {c |}{col 25}{res}{space 2}-4.787751{col 37}{space 2} .5338299{col 48}{space 1}   -8.97{col 57}{space 3}0.000{col 65}{space 4}-5.834038{col 78}{space 3}-3.741464
{txt}{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}_all>polnum            {col 25}{txt}{c |}
{space 14}var(_cons){c |}{col 25}{res}{space 2} .2258311{col 37}{space 2} .1018376{col 65}{space 4} .0933121{col 78}{space 3} .5465495
{txt}{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}statenum               {col 25}{txt}{c |}
{space 14}var(_cons){c |}{col 25}{res}{space 2} .6182244{col 37}{space 2} .2165937{col 65}{space 4}  .311121{col 78}{space 3} 1.228466
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{txt}LR test vs. logistic model: {txt}chi2({res}2{txt}) ={res} 554.39{col 59}{txt}Prob > chi2 ={res}{col 73}0.0000

{txt}{p 0 6 4 79}Note: {help j_mixedlr##|_new:LR test is conservative} and provided only for reference.{p_end}

{com}. 
{txt}end of do-file

{com}. use "C:\Users\toddm\Downloads\Diffusion and DMAs\Final Submission Files\Replication Files\ENAPD State Representativeness.dta", clear

. ttest pci, by(enapd)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
      No {c |}{res}{col 12}     30{col 22} 28128.07{col 34} 696.0993{col 46} 3812.693{col 58} 26704.38{col 70} 29551.75
     {txt}Yes {c |}{res}{col 12}     20{col 22} 26687.35{col 34} 918.0544{col 46} 4105.664{col 58} 24765.84{col 70} 28608.86
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}     50{col 22} 27551.78{col 34} 559.4251{col 46} 3955.733{col 58} 26427.57{col 70} 28675.99
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} 1440.717{col 34}  1134.86{col 58}-841.0733{col 70} 3722.507
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}No{txt}) - mean({res}Yes{txt})                                   t = {res}  1.2695
{txt}Ho: diff = 0                                     degrees of freedom = {res}      48

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.8948         {txt}Pr(|T| > |t|) = {res}0.2104          {txt}Pr(T > t) = {res}0.1052

{com}. 
. ttest obama, by(enapd)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
      No {c |}{res}{col 12}     30{col 22} .5121333{col 34} .0185125{col 46} .1013974{col 58} .4742709{col 70} .5499957
     {txt}Yes {c |}{res}{col 12}     20{col 22}   .49355{col 34} .0192298{col 46} .0859985{col 58} .4533015{col 70} .5337985
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}     50{col 22}    .5047{col 34} .0134441{col 46} .0950644{col 58}  .477683{col 70}  .531717
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} .0185833{col 34} .0275971{col 58}-.0369043{col 70} .0740709
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}No{txt}) - mean({res}Yes{txt})                                   t = {res}  0.6734
{txt}Ho: diff = 0                                     degrees of freedom = {res}      48

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.7480         {txt}Pr(|T| > |t|) = {res}0.5039          {txt}Pr(T > t) = {res}0.2520

{com}. 
. ttest blackpct, by(enapd)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
      No {c |}{res}{col 12}     30{col 22}    .0846{col 34} .0151176{col 46} .0828025{col 58} .0536811{col 70} .1155189
     {txt}Yes {c |}{res}{col 12}     20{col 22}    .1355{col 34} .0236224{col 46} .1056426{col 58} .0860577{col 70} .1849423
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}     50{col 22}   .10496{col 34} .0134311{col 46} .0949724{col 58} .0779691{col 70} .1319509
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22}   -.0509{col 34} .0267082{col 58}-.1046005{col 70} .0028005
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}No{txt}) - mean({res}Yes{txt})                                   t = {res} -1.9058
{txt}Ho: diff = 0                                     degrees of freedom = {res}      48

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.0313         {txt}Pr(|T| > |t|) = {res}0.0627          {txt}Pr(T > t) = {res}0.9687

{com}. 
. ttest hisppct, by(enapd)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
      No {c |}{res}{col 12}     30{col 22}    .1355{col 34} .0220233{col 46} .1206265{col 58} .0904573{col 70} .1805427
     {txt}Yes {c |}{res}{col 12}     20{col 22}   .06935{col 34} .0089042{col 46}  .039821{col 58} .0507132{col 70} .0879868
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}     50{col 22}   .10904{col 34} .0143514{col 46} .1014799{col 58} .0801997{col 70} .1378803
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22}   .06615{col 34}  .028016{col 58} .0098201{col 70} .1224799
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}No{txt}) - mean({res}Yes{txt})                                   t = {res}  2.3612
{txt}Ho: diff = 0                                     degrees of freedom = {res}      48

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.9888         {txt}Pr(|T| > |t|) = {res}0.0223          {txt}Pr(T > t) = {res}0.0112

{com}. 
. ttest squire, by(enapd)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
      No {c |}{res}{col 12}     30{col 22} .1933333{col 34} .0239575{col 46} .1312206{col 58} .1443347{col 70} .2423319
     {txt}Yes {c |}{res}{col 12}     20{col 22}    .1713{col 34}  .019192{col 46} .0858291{col 58} .1311307{col 70} .2114693
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}     50{col 22}   .18452{col 34} .0162272{col 46} .1147436{col 58} .1519102{col 70} .2171298
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} .0220333{col 34} .0333154{col 58}-.0449518{col 70} .0890185
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}No{txt}) - mean({res}Yes{txt})                                   t = {res}  0.6614
{txt}Ho: diff = 0                                     degrees of freedom = {res}      48

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.7442         {txt}Pr(|T| > |t|) = {res}0.5115          {txt}Pr(T > t) = {res}0.2558

{com}. 
. ttest legsize_tot, by(enapd)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
      No {c |}{res}{col 12}     30{col 22} 140.9333{col 34} 13.01705{col 46} 71.29731{col 58} 114.3105{col 70} 167.5562
     {txt}Yes {c |}{res}{col 12}     20{col 22}   157.75{col 34} 8.393567{col 46} 37.53717{col 58} 140.1821{col 70} 175.3179
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}     50{col 22}   147.66{col 34} 8.513644{col 46} 60.20055{col 58} 130.5512{col 70} 164.7688
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22}-16.81667{col 34} 17.38991{col 58}-51.78143{col 70}  18.1481
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}No{txt}) - mean({res}Yes{txt})                                   t = {res} -0.9670
{txt}Ho: diff = 0                                     degrees of freedom = {res}      48

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.1692         {txt}Pr(|T| > |t|) = {res}0.3384          {txt}Pr(T > t) = {res}0.8308

{com}. 
. ttest innov, by(enapd)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
      No {c |}{res}{col 12}     30{col 22}    .2846{col 34} .0133807{col 46}  .073289{col 58} .2572334{col 70} .3119666
     {txt}Yes {c |}{res}{col 12}     20{col 22}   .27095{col 34} .0148536{col 46} .0664272{col 58} .2398611{col 70} .3020389
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}     50{col 22}   .27914{col 34} .0099353{col 46} .0702535{col 58} .2591742{col 70} .2991058
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22}   .01365{col 34} .0203957{col 58}-.0273582{col 70} .0546582
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}No{txt}) - mean({res}Yes{txt})                                   t = {res}  0.6693
{txt}Ho: diff = 0                                     degrees of freedom = {res}      48

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.7467         {txt}Pr(|T| > |t|) = {res}0.5065          {txt}Pr(T > t) = {res}0.2533

{com}. 
. ttest termlim, by(enapd)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
      No {c |}{res}{col 12}     30{col 22}       .3{col 34} .0850963{col 46} .4660916{col 58} .1259585{col 70} .4740415
     {txt}Yes {c |}{res}{col 12}     20{col 22}       .3{col 34} .1051315{col 46} .4701623{col 58} .0799572{col 70} .5200428
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}     50{col 22}       .3{col 34} .0654654{col 46}   .46291{col 58} .1684424{col 70} .4315576
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22}        0{col 34} .1350154{col 58}-.2714667{col 70} .2714667
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}No{txt}) - mean({res}Yes{txt})                                   t = {res}  0.0000
{txt}Ho: diff = 0                                     degrees of freedom = {res}      48

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.5000         {txt}Pr(|T| > |t|) = {res}1.0000          {txt}Pr(T > t) = {res}0.5000

{com}. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\toddm\Downloads\Diffusion and DMAs\Final Submission Files\Replication Files\Log Final.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res} 6 Apr 2024, 00:04:05
{txt}{.-}
{smcl}
{txt}{sf}{ul off}